{"id":599466,"date":"2019-07-24T07:18:53","date_gmt":"2019-07-24T14:18:53","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-event&p=599466"},"modified":"2025-08-06T11:56:23","modified_gmt":"2025-08-06T18:56:23","slug":"acl-2019","status":"publish","type":"msr-event","link":"https:\/\/www.microsoft.com\/en-us\/research\/event\/acl-2019\/","title":{"rendered":"Microsoft at ACL 2019"},"content":{"rendered":"\n\n

Venue:\u00a0<\/strong>Fortezza da Basso (opens in new tab)<\/span><\/a><\/p>\n

Website:<\/strong> ACL 2019 (opens in new tab)<\/span><\/a>Opens in a new tab<\/span><\/p>\n

Microsoft is excited to be a Diamond sponsor of the (opens in new tab)<\/span><\/a> 57th Annual Meeting of the Association for Computational Linguistics (ACL) (opens in new tab)<\/span><\/a>. We will have over 50 Microsoft attendees present at the conference. Stop by our booth to chat with our experts, see demos of our latest research and find out about career opportunities (opens in new tab)<\/span><\/a>\u00a0with Microsoft.<\/p>\n

Microsoft attendees<\/h3>\n

Adam Trischler<\/a>
\nAhmed Awadallah<\/a>
\nAmitha Gopidi
\nAndrew McNamara<\/a>
\nAnoop Kunchukuttan
\nAsli Celikyilmaz<\/a>
\nBill Dolan<\/a>
\nCan Xu
\nChenguang Zhu<\/a>
\nChin-Yew Lin<\/a>
\nDan Klein
\nDon Graney
\nDuyu Tang<\/a>
\nEhsan Zare Borzeshi
\nEric Lin<\/a>
\nFitzgerald Nicole
\nGraney Don
\nHal Daume III<\/a>
\nHannes Schulz<\/a>
\nHany Hassan<\/a>
\nJianfeng Gao<\/a>
\nJianguang Lou
\nJin-Ge Yao
\nJingjing Liu<\/a>
\nJinpeng Wang<\/a>
\nJinxi Xu
\nKaheer Suleman<\/a>
\nLei Cui<\/a>
\nLi Dong
\nLinjun Shou
\nMahmoud Adada<\/a>
\nMark Bolin
\nMichel Galley<\/a>
\nMing Gong
\nMohamad Alkhoujeh
\nNan Duan<\/a>
\nPeter Potash<\/a>
\nRahul Mehrotra<\/a>
\nSaid Bleik
\nSamira Shabanian<\/a>
\nShane Peckham
\nShuming Ma
\nSungjin Lee
\nTao Ge
\nTao Qin<\/a>
\nTatsuro Oya<\/a>
\nTimothy Hazen
\nVighnesh Shiv
\nVikas Bahirwani
\nWenhui Zhang
\nWenhui Wang
\nXiang Gao<\/a>
\nXiaoyu Qi
\nXihui Lin<\/a>
\nXingxing Zhang
\nXiubo Geng
\nXu Tan<\/a>
\nYadollah Yaghoobzadeh<\/a>
\nYingce Xia<\/a>Opens in a new tab<\/span><\/p>\n

Monday, July 29<\/h3>\n

10:30 \u2013 10:50 | Session 1A: Dialogue and Interactive Systems 1 – Neural Conversation Models
\nOne Time of Interaction May Not Be Enough: Go Deep with an Interaction-over-Interaction Network for Response Selection in Dialogues<\/strong>
\nChongyang Tao, Wei Wu<\/strong><\/a>, Can Xu<\/strong>, Wenpeng Hu, Dongyan Zhao, Rui Yan<\/p>\n

10:30 \u2013 10:50 | Session 1D: Machine Translation 1
\nUnsupervised Pivot Translation for Distant Languages<\/strong>
\nYichong Leng, Xu Tan<\/strong><\/a>, Tao Qin<\/strong><\/a>, Xiang-Yang Li, Tie-Yan Liu<\/strong><\/a><\/p>\n

10:30 \u2013 12:10 | Applications | Poster Session 1
\nNeural News Recommendation with Long- and Short-term User Representations<\/strong>
\nMingxiao An, Fangzhao Wu<\/strong><\/a>, Chuhan Wu, Kun Zhang, Zheng Liu<\/strong>, Xing Xie<\/strong><\/a><\/p>\n

10:30 \u2013 12:10 | Resources and Evaluation | Poster Session 1
\nErrudite: Scalable, Reproducible, and Testable Error Analysis<\/strong>
\nTongshuang Wu, Marco Tulio Ribeiro<\/strong>, Jeffrey Heer, Daniel Weld<\/p>\n

11:50 \u2013 12:10 | Session 1B: Sentence-level Semantics
\nLearning Compressed Sentence Representations for On-Device Text Processing<\/strong>
\nDinghan Shen, Pengyu Cheng, Dhanasekar Sundararaman, Xinyuan Zhang, Qian Yang, Meng Tang, Asli Celikyilmaz<\/strong><\/a>, Lawrence Carin<\/p>\n

13:50 \u2013 14:10 | Session 2B: Textual Inference and Other Areas of Semantics
\nCoupling Retrieval and Meta-Learning for Context-Dependent Semantic Parsing<\/strong>
\nDaya Guo, Duyu Tang<\/strong><\/a>, Nan Duan<\/strong><\/a>, Ming Zhou<\/strong><\/a>, Jian Yin<\/p>\n

13:50 \u2013 15:30 | Information Extraction and Text Mining | Poster Session 2
\nJoint Type Inference on Entities and Relations via Graph Convolutional Networks<\/strong>
\nChangzhi Sun, Yeyun Gong<\/strong>, Nan Duan<\/strong><\/a>, Ming Gong<\/strong>, Daxin Jiang<\/strong>, Shiliang Sun, Man Lan, Yuanbin Wu<\/p>\n

13:50 \u2013 15:30 | Applications | Poster Session 2
\nNeural News Recommendation with Topic-Aware News Representation<\/strong>
\nChuhan Wu, Fangzhao Wu<\/strong><\/a>, Mingxiao An, Yongfeng Huang, Xing Xie<\/strong><\/a><\/p>\n

16:00 \u2013 17:40 | Generation | Poster Session 3
\nData-to-text Generation with Entity Modeling<\/strong>
\nRatish Puduppully, Li Dong<\/strong>, Mirella Lapata<\/p>\n

16:00 \u2013 17:40 | Generation | Poster Session 3
\nTowards Generating Long and Coherent Text with Multi-Level Latent Variable Models<\/strong>
\nDinghan Shen, Asli Celikyilmaz<\/strong><\/a>, Yizhe Zhang<\/strong><\/a>, Liqun Chen, Xin Wang, Jianfeng Gao<\/strong><\/a>, Lawrence Carin<\/p>\n

16:40 \u2013 17:00 | Session 3A: Bias in Language Processing
\nCounterfactual Data Augmentation for Mitigating Gender Stereotypes in Languages with Rich Morphology<\/strong>
\nRan Zmigrod, Sebastian J. Mielke, Hanna Wallach<\/strong><\/a>, Ryan Cotterell<\/p>\n

16:40 \u2013 17:00 | Session 3B: Word-level Semantics 1
\nUnsupervised Discovery of Gendered Language through Latent-Variable Modeling<\/strong>
\nAlexander Miserlis Hoyle, Lawrence Wolf-Sonkin, Hanna Wallach<\/strong><\/a>, Isabelle Augenstein, Ryan Cotterell<\/p>\n

Tuesday, July 30<\/h3>\n

10:30 \u2013 10:50 | Session 4C: Evaluation
\nSentence Mover’s Similarity: Automatic Evaluation for Multi-Sentence Texts<\/strong>
\nElizabeth Clark, Asli Celikyilmaz<\/a><\/strong>, Noah A. Smith<\/p>\n

10:30 \u2013 12:10 | Multilinguality | Poster Session 4
\nMulti-Source Cross-Lingual Model Transfer: Learning What to Share<\/strong>
\nXilun Chen, Ahmed Hassan Awadallah<\/strong><\/a>, Hany Hassan<\/strong><\/a>, Wei Wang<\/strong><\/a>, Claire Cardie<\/p>\n

10:30 \u2013 12:10 | Word-level Semantics | Poster Session 4
\nEmbedding Imputation with Grounded Language Information<\/strong>
\nZiyi Yang, Chenguang Zhu<\/strong><\/a>, Vin Sachidananda, Eric Darve<\/p>\n

10:30 \u2013 12:10 | Word-level Semantics | Poster Session 4
\nBERT-based Lexical Substitution<\/strong>
\nWangchunshu Zhou<\/strong>, Tao Ge<\/strong>, Ke Xu, Furu Wei<\/strong><\/a>, Ming Zhou<\/strong><\/a><\/p>\n

11:10 \u2013 11:30 | Session 4B: Question Answering 1 – Multi-Hop
\nExplore, Propose, and Assemble: An Interpretable Model for Multi-Hop Reading Comprehension<\/strong>
\nYichen Jiang, Nitish Joshi, Yen-Chun Chen<\/strong>, Mohit Bansal<\/p>\n

11:30 \u2013 11:43 | Session 4A: Dialogue and Generation
\nA Simple Recipe towards Reducing Hallucination in Neural Surface Realisation<\/strong>
\nFeng Nie, Jin-Ge Yao<\/strong>, Jinpeng Wang<\/strong><\/a>, Rong Pan, Chin-Yew Lin<\/strong><\/a><\/p>\n

13:50 \u2013 15:30 | Machine Learning | Poster Session 5
\nTowards Language Agnostic Universal Representations<\/strong>
\nArmen Aghajanyan, Xia Song<\/strong>, Saurabh Tiwary<\/strong><\/p>\n

13:50 \u2013 15:30 | Dialogue and Interactive Systems | Poster Session 5
\nBudgeted Policy Learning for Task-Oriented Dialogue Systems<\/strong>
\nZhirui Zhang, Xiujun Li<\/strong><\/a>, Jianfeng Gao<\/strong><\/a>, Enhong Chen<\/p>\n

13:50 \u2013 15:30 | Dialogue and Interactive Systems | Poster Session 5
\nRetrieval-Enhanced Adversarial Training for Neural Response Generation<\/strong>
\nQingfu Zhu, Lei Cui<\/strong><\/a>, Wei-Nan Zhang, Furu Wei<\/strong><\/a>, Ting Liu<\/p>\n

13:50 \u2013 15:30 | Dialogue and Interactive Systems | Poster Session 5
\nLearning a Matching Model with Co-teaching for Multi-turn Response Selection in Retrieval-based Dialogue Systems<\/strong>
\nJiazhan Feng, Chongyang Tao, Wei Wu<\/strong><\/a>, Yansong Feng, Dongyan Zhao, Rui Yan<\/p>\n

13:50 \u2013 15:30 | Resources and Evaluation | Poster Session 5
\nThe KnowRef Coreference Corpus: Removing Gender and Number Cues for Difficult Pronominal Anaphora Resolution<\/strong>
\nAli Emami, Paul Trichelair, Adam Trischler<\/strong><\/a>, Kaheer Suleman<\/strong><\/a>, Hannes Schulz<\/strong><\/a>, Jackie Chi Kit Cheung<\/p>\n

14:50 \u2013 15:10 | Session 5D: Sentiment Analysis and Argument Mining 2
\nExploring Sequence-to-Sequence Learning in Aspect Term Extraction<\/strong>
\nDehong Ma, Sujian Li, Fangzhao Wu<\/strong><\/a>, Xing Xie<\/strong><\/a>, Houfeng Wang<\/p>\n

Wednesday, July 31<\/h3>\n

10:30 \u2013 12:10 | Sentence-level Semantics | Poster Session 6
\nMulti-Task Deep Neural Networks for Natural Language Understanding<\/strong>
\nXiaodong Liu<\/strong><\/a>, Pengcheng He<\/strong>, Weizhu Chen<\/strong>, Jianfeng Gao<\/strong><\/a><\/p>\n

10:30 \u2013 12:10 | Textual Inference and Other Areas of Semantics | Poster Session 6
\nCOMET: Commonsense Transformers for Automatic Knowledge Graph Construction<\/strong>
\nAntoine Bosselut, Hannah Rashkin, Maarten Sap, Chaitanya Malaviya, Asli Celikyilmaz<\/strong><\/a>, Yejin Choi<\/p>\n

10:30 \u2013 12:10 | Sentence-level Semantics | Poster Session 6
\nTowards Complex Text-to-SQL in Cross-Domain Database with Intermediate Representation<\/strong>
\nJiaqi Guo, Zecheng Zhan, Yan Gao<\/strong>, Yan Xiao<\/strong>, Jian-Guang Lou<\/strong><\/a>, Ting Liu, Dongmei Zhang<\/strong><\/a><\/p>\n

10:50 \u2013 11:10 | Session 6B: Question Answering 2
\nLearning to Ask Unanswerable Questions for Machine Reading Comprehension<\/strong>
\nHaichao Zhu, Li Dong<\/strong>, Furu Wei<\/strong><\/a>, Wenhui Wang<\/strong>, Bing Qin, Ting Liu<\/p>\n

13:50 \u2013 14:10 | Session 7E: Summarization 2
\nHIBERT: Document Level Pre-training of Hierarchical Bidirectional Transformers for Document Summarization<\/strong>
\nXingxing Zhang<\/strong>, Furu Wei<\/strong><\/a>, Ming Zhou<\/strong><\/a><\/p>\n

13:50 \u2013 15:30 | Machine Learning | Poster Session 7
\nSoft Contextual Data Augmentation for Neural Machine Translation<\/strong>
\nJinhua Zhu, Fei Gao, Lijun Wu, Yingce Xia<\/strong><\/a>, Tao Qin<\/strong><\/a>, Wengang Zhou, Xueqi Cheng, Tie-Yan Liu<\/strong><\/a><\/p>\n

13:50 \u2013 15:30 | Information Extraction and Text Mining | Poster Session 7
\nTowards Improving Neural Named Entity Recognition with Gazetteers<\/strong>
\nTianyu Liu, Jin-Ge Yao<\/strong>, Chin-Yew Lin<\/strong><\/a><\/p>\n

13:50 \u2013 15:30 | Information Extraction and Text Mining | Poster Session 7
\nImproving Textual Network Embedding with Global Attention via Optimal Transport<\/strong>
\nLiqun Chen, Guoyin Wang, Chenyang Tao, Dinghan Shen, Pengyu Cheng, Xinyuan Zhang, Wenlin Wang, Yizhe Zhang<\/strong><\/a>, Lawrence Carin<\/p>\n

13:50 \u2013 15:30 | Machine Learning | Poster Session 7
\nDepth Growing for Neural Machine Translation<\/strong>
\nLijun Wu, Yiren Wang, Yingce Xia<\/strong><\/a>, Fei Tian<\/strong>, Fei Gao, Tao Qin<\/strong><\/a>, Jianhuang Lai, Tie-Yan Liu<\/strong><\/a><\/p>\n

13:50 \u2013 15:30 | Dialogue and Interactive Systems | Poster Session 7
\nConversing by Reading: Contentful Neural Conversation with On-demand Machine Reading<\/strong>
\nLianhui Qin, Michel Galley<\/strong><\/a>, Chris Brockett<\/strong><\/a>, Xiaodong Liu<\/strong><\/a>, Xiang Gao<\/strong><\/a>, Bill Dolan<\/strong><\/a>, Yejin Choi, Jianfeng Gao<\/strong><\/a><\/p>\n

13:50 \u2013 15:30 | Dialogue and Interactive Systems | Poster Session 7
\nNeural Response Generation with Meta-words<\/strong>
\nCan Xu<\/strong>, Wei Wu<\/strong><\/a>, Chongyang Tao, Huang Hu<\/strong>, Matt Schuerman<\/strong>, Ying Wang<\/strong><\/p>\n

14:30 \u2013 14:50 | Session 7B: Question Answering 3
\nSimple and Effective Curriculum Pointer-Generator Networks for Reading Comprehension over Long Narratives<\/strong>
\nYi Tay, Shuohang Wang, Anh Tuan Luu, Jie Fu, Minh C. Phan, Xingdi Yuan<\/strong><\/a>, Jinfeng Rao, Siu Cheung Hui, Aston Zhang<\/p>\n

16:00 \u2013 17:40 | Vision, Robotics, Multimodal, Grounding and Speech | Poster Session 8
\nDense Procedure Captioning in Narrated Instructional Videos<\/strong>
\nBotian Shi, Lei Ji<\/strong><\/a>, Yaobo Liang<\/strong><\/a>, Zhendong Niu, Nan Duan<\/strong><\/a>, Ming Zhou<\/strong><\/a><\/p>\n

16:00 \u2013 17:40 | Vision, Robotics, Multimodal, Grounding and Speech | Poster Session 8
\nMulti-step Reasoning via Recurrent Dual Attention for Visual Dialog<\/strong>
\nZhe Gan<\/strong>, Yu Cheng<\/strong>, Ahmed Kholy<\/strong>, Linjie Li<\/strong>, Jingjing Liu<\/strong><\/a>, Jianfeng Gao<\/strong><\/a><\/p>\n

16:00 \u2013 17:40 | Question Answering | Poster Session 8
\nAre Red Roses Red? Evaluating Consistency of Question-Answering Models<\/strong>
\nMarco Tulio Ribeiro<\/strong>, Carlos Guestrin, Sameer Singh<\/p>\n

16:00 \u2013 17:40 | Generation | Poster Session 8
\nAutomatic Grammatical Error Correction for Sequence-to-sequence Text Generation: An Empirical Study<\/strong>
\nTao Ge<\/strong>, Xingxing Zhang<\/strong>, Furu Wei<\/strong><\/a>, Ming Zhou<\/strong><\/a><\/p>\n

16:20 \u2013 16:40 | Session 8C: Resources and Evaluation
\nProbing for Semantic Classes: Diagnosing the Meaning Content of Word Embeddings<\/strong>
\nYadollah Yaghoobzadeh<\/strong>, Katharina Kann, T. J. Hazen<\/strong><\/a>, Eneko Agirre, Hinrich Sch\u00fctze<\/p>\n

16:40 \u2013 17:00 | Session 8A: Dialogue and Interactive Systems 3 – New Tasks
\nTarget-Guided Open-Domain Conversation<\/strong>
\nJianheng Tang, Zhiting Hu, Tiancheng Zhao, Chenyan Xiong<\/strong><\/a>, Xiaodan Liang, Eric Xing<\/p>\n

16:40 \u2013 17:00 | Session 8B: Word-level Semantics 2
\nWord2Sense: Sparse Interpretable Word Embeddings<\/strong>
\nAbhishek Panigrahi<\/strong>, Harsha Vardhan Simhadri, Chiranjib BhattacharyyaOpens in a new tab<\/span><\/p>\n

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\n\t\t\t\t\t\t\tApplied Researcher<\/a>\n\t\t\t\t\t<\/h3>\n\n\t\t
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Type<\/strong>: Full-time<\/p>

Lab\/Location<\/strong>: Bellevue, Washington<\/p>

Do you like to work in an environment where you can implement new ideas as well as transfer technology from research into the product? You can help us create services that understand what people say and give them the answers that they want and complete the tasks that they need….<\/p>

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Type<\/strong>: Full-time<\/p>

Lab\/Location<\/strong>: Bellevue, Washington<\/p>

Entity Understanding team within Bing search engine is responsible for providing the most relevant answer to user queries related with the real-world entities like people, movies, songs, locations and more. It helps users find the facts about entities quickly without need for searching through web results…<\/p>

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Type<\/strong>: Full-time<\/p>

Lab\/Location<\/strong>: Multiple<\/p>

The Semantic Machines group is working to reshape human-computer interaction with conversational AI in our three offices: Boston MA, Berkeley CA, and Bellevue WA. We’re looking for software engineers to work hand-in-hand with our data and research teams to develop new approaches to solving deep…<\/p>

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Type<\/strong>: Full-time<\/p>

Lab\/Location<\/strong>: Sunnyvale, California<\/p>

What if your job description were simply \u201cMake tomorrow better?\u201d That\u2019s the essence of roles within our Bing Team. Every day, we bring an insatiable curiosity to the table, challenging ourselves to reimagine what is and what can be. We build on what\u2019s come before to create what\u2019s next. We drive machine intelligence. We help…<\/p>

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Type<\/strong>: Full-time<\/p>

Lab\/Location<\/strong>: Sunnyvale, California<\/p>

Online Advertising is one of the fastest growing businesses on the Internet today, with about $70 billion of a $600 billion advertising market already online. Search engines, web publishers, major ad networks, and ad exchanges are now serving billions of ad impressions per day and generating terabytes of user…<\/p>

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Type<\/strong>: Full-time<\/p>

Lab\/Location<\/strong>: Bellevue, Washington<\/p>

BAG AI is working on cutting-edge AI models for business applications. The team is focused on applying advanced natural language processing (NLP) techniques to customer-facing products, such as conversational AI, question answering, entity recognition, and recommendation. We are a hybrid team between research and…<\/p>

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Type<\/strong>: Full-time<\/p>

Lab\/Location<\/strong>: Bellevue, Washington<\/p>

The Bing Search & AI team needs strong PMs to lead a team of engineers and researchers to apply the cutting edge of artificial intelligence and deep learning technologies to solve some challenging business problems for Bing and Microsoft. Specifically, we want to take state of the art natural language understanding…<\/p>

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Type<\/strong>: Full-time<\/p>

Lab\/Location<\/strong>: Redmond, Washington<\/p>

Project EmpowerMD is an incubation team at Microsoft Healthcare. Our mission is to empower physicians by building a voice-enabled intelligent assistant, leveraging the best in speech-to-text, NLP, and other ML technologies. We are a fast-moving multi-disciplinary team that is deeply engaged in the healthcare space with ample…<\/p>

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Type<\/strong>: Full-time<\/p>

Lab\/Location<\/strong>: Multiple<\/p>

Semantic Machines is hiring a few extremely talented researchers to join our world-class team in Boston, MA, Bellevue, WA, and Berkeley, CA. Our team has advanced the state of the art in natural language processing, speech processing, deep learning, video game AI, and even computational historical linguistics. We’re…<\/p>

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Type<\/strong>: Full-time<\/p>

Lab\/Location<\/strong>: Redmond, Washington<\/p>

Artificial intelligence (AI) is dramatically transforming people\u2019s work and life now. Speech technology is in the center of the transform. Speech recognition and text to speech (TTS) are essential pieces to enable intelligent agent in the AI era. Microsoft\u2019s mission is to build human parity TTS voices for as many languages…<\/p>

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Type<\/strong>: Internship<\/p>

Lab\/Location<\/strong>: Redmond, Washington<\/p>

Artificial intelligence (AI) is dramatically transforming people\u2019s work and life now. Speech technology is in the center of the transform. Speech recognition and text to speech (TTS) are essential pieces to enable intelligent agent in the AI era. Microsoft\u2019s mission is to build human parity TTS voices for as many…<\/p>

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Type<\/strong>: Full-time<\/p>

Lab\/Location<\/strong>: Berkeley, California<\/p>

Semantic Machines is hiring a few extremely talented researchers to join our world-class team in Boston, MA, Bellevue, WA, and Berkeley, CA. Our team has advanced the state of the art in natural language processing, speech processing, deep learning, video game AI, and even computational historical linguistics…<\/p>

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Type<\/strong>: Internship<\/p>

Lab\/Location<\/strong>: Redmond, Washington<\/p>

Microsoft provides a nurturing environment to support passionate researchers and engineers in AI technology innovation. We are seeking candidates excelling in deep thinking research aspects and fast-paced entrepreneurial execution…<\/p>

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Type<\/strong>: Full-time<\/p>

Lab\/Location<\/strong>: Redmond, Washington<\/p>

Microsoft provides a nurturing environment to support passionate researchers and engineers in AI technology innovation. We are seeking candidates excelling in deep thinking research aspects and fast-paced entrepreneurial execution…<\/p>

\t\t\t<\/div>\n\t\t<\/div>\n\n\t<\/div>\n<\/article>\n<\/p>\t\t\t<\/div>\n\t\t<\/div>\n\t\tOpens in a new tab<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

Venue:\u00a0Fortezza da Basso (opens in new tab) Website: ACL 2019 (opens in new tab)Opens in a new tab Microsoft is excited to be a Diamond sponsor of the (opens in new tab) 57th Annual Meeting of the Association for Computational Linguistics (ACL) (opens in new tab). We will have over 50 Microsoft attendees present at […]<\/p>\n","protected":false},"featured_media":599481,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr_startdate":"2019-07-28","msr_enddate":"2019-08-02","msr_location":"Florence, Italy","msr_expirationdate":"","msr_event_recording_link":"","msr_event_link":"","msr_event_link_redirect":false,"msr_event_time":"","msr_hide_region":false,"msr_private_event":false,"msr_hide_image_in_river":0,"footnotes":""},"research-area":[13545],"msr-region":[239178],"msr-event-type":[197941],"msr-video-type":[],"msr-locale":[268875],"msr-program-audience":[],"msr-post-option":[],"msr-impact-theme":[],"class_list":["post-599466","msr-event","type-msr-event","status-publish","has-post-thumbnail","hentry","msr-research-area-human-language-technologies","msr-region-europe","msr-event-type-conferences","msr-locale-en_us"],"msr_about":"\n\n

Venue:\u00a0<\/strong>Fortezza da Basso (opens in new tab)<\/span><\/a><\/p>\n

Website:<\/strong> ACL 2019 (opens in new tab)<\/span><\/a>Opens in a new tab<\/span><\/p>\n

Microsoft is excited to be a Diamond sponsor of the<\/a> 57th Annual Meeting of the Association for Computational Linguistics (ACL)<\/a>. We will have over 50 Microsoft attendees present at the conference. Stop by our booth to chat with our experts, see demos of our latest research and find out about career opportunities<\/a>\u00a0with Microsoft.<\/p>\n

Microsoft attendees<\/h3>\n

Adam Trischler<\/a>
\nAhmed Awadallah<\/a>
\nAmitha Gopidi
\nAndrew McNamara<\/a>
\nAnoop Kunchukuttan
\nAsli Celikyilmaz<\/a>
\nBill Dolan<\/a>
\nCan Xu
\nChenguang Zhu<\/a>
\nChin-Yew Lin<\/a>
\nDan Klein
\nDon Graney
\nDuyu Tang<\/a>
\nEhsan Zare Borzeshi
\nEric Lin<\/a>
\nFitzgerald Nicole
\nGraney Don
\nHal Daume III<\/a>
\nHannes Schulz<\/a>
\nHany Hassan<\/a>
\nJianfeng Gao<\/a>
\nJianguang Lou
\nJin-Ge Yao
\nJingjing Liu<\/a>
\nJinpeng Wang<\/a>
\nJinxi Xu
\nKaheer Suleman<\/a>
\nLei Cui<\/a>
\nLi Dong
\nLinjun Shou
\nMahmoud Adada<\/a>
\nMark Bolin
\nMichel Galley<\/a>
\nMing Gong
\nMohamad Alkhoujeh
\nNan Duan<\/a>
\nPeter Potash<\/a>
\nRahul Mehrotra<\/a>
\nSaid Bleik
\nSamira Shabanian<\/a>
\nShane Peckham
\nShuming Ma
\nSungjin Lee
\nTao Ge
\nTao Qin<\/a>
\nTatsuro Oya<\/a>
\nTimothy Hazen
\nVighnesh Shiv
\nVikas Bahirwani
\nWenhui Zhang
\nWenhui Wang
\nXiang Gao<\/a>
\nXiaoyu Qi
\nXihui Lin<\/a>
\nXingxing Zhang
\nXiubo Geng
\nXu Tan<\/a>
\nYadollah Yaghoobzadeh<\/a>
\nYingce Xia<\/a>Opens in a new tab<\/span><\/p>\n

Monday, July 29<\/h3>\n

10:30 \u2013 10:50 | Session 1A: Dialogue and Interactive Systems 1 – Neural Conversation Models
\nOne Time of Interaction May Not Be Enough: Go Deep with an Interaction-over-Interaction Network for Response Selection in Dialogues<\/strong>
\nChongyang Tao, Wei Wu<\/strong><\/a>, Can Xu<\/strong>, Wenpeng Hu, Dongyan Zhao, Rui Yan<\/p>\n

10:30 \u2013 10:50 | Session 1D: Machine Translation 1
\nUnsupervised Pivot Translation for Distant Languages<\/strong>
\nYichong Leng, Xu Tan<\/strong><\/a>, Tao Qin<\/strong><\/a>, Xiang-Yang Li, Tie-Yan Liu<\/strong><\/a><\/p>\n

10:30 \u2013 12:10 | Applications | Poster Session 1
\nNeural News Recommendation with Long- and Short-term User Representations<\/strong>
\nMingxiao An, Fangzhao Wu<\/strong><\/a>, Chuhan Wu, Kun Zhang, Zheng Liu<\/strong>, Xing Xie<\/strong><\/a><\/p>\n

10:30 \u2013 12:10 | Resources and Evaluation | Poster Session 1
\nErrudite: Scalable, Reproducible, and Testable Error Analysis<\/strong>
\nTongshuang Wu, Marco Tulio Ribeiro<\/strong>, Jeffrey Heer, Daniel Weld<\/p>\n

11:50 \u2013 12:10 | Session 1B: Sentence-level Semantics
\nLearning Compressed Sentence Representations for On-Device Text Processing<\/strong>
\nDinghan Shen, Pengyu Cheng, Dhanasekar Sundararaman, Xinyuan Zhang, Qian Yang, Meng Tang, Asli Celikyilmaz<\/strong><\/a>, Lawrence Carin<\/p>\n

13:50 \u2013 14:10 | Session 2B: Textual Inference and Other Areas of Semantics
\nCoupling Retrieval and Meta-Learning for Context-Dependent Semantic Parsing<\/strong>
\nDaya Guo, Duyu Tang<\/strong><\/a>, Nan Duan<\/strong><\/a>, Ming Zhou<\/strong><\/a>, Jian Yin<\/p>\n

13:50 \u2013 15:30 | Information Extraction and Text Mining | Poster Session 2
\nJoint Type Inference on Entities and Relations via Graph Convolutional Networks<\/strong>
\nChangzhi Sun, Yeyun Gong<\/strong>, Nan Duan<\/strong><\/a>, Ming Gong<\/strong>, Daxin Jiang<\/strong>, Shiliang Sun, Man Lan, Yuanbin Wu<\/p>\n

13:50 \u2013 15:30 | Applications | Poster Session 2
\nNeural News Recommendation with Topic-Aware News Representation<\/strong>
\nChuhan Wu, Fangzhao Wu<\/strong><\/a>, Mingxiao An, Yongfeng Huang, Xing Xie<\/strong><\/a><\/p>\n

16:00 \u2013 17:40 | Generation | Poster Session 3
\nData-to-text Generation with Entity Modeling<\/strong>
\nRatish Puduppully, Li Dong<\/strong>, Mirella Lapata<\/p>\n

16:00 \u2013 17:40 | Generation | Poster Session 3
\nTowards Generating Long and Coherent Text with Multi-Level Latent Variable Models<\/strong>
\nDinghan Shen, Asli Celikyilmaz<\/strong><\/a>, Yizhe Zhang<\/strong><\/a>, Liqun Chen, Xin Wang, Jianfeng Gao<\/strong><\/a>, Lawrence Carin<\/p>\n

16:40 \u2013 17:00 | Session 3A: Bias in Language Processing
\nCounterfactual Data Augmentation for Mitigating Gender Stereotypes in Languages with Rich Morphology<\/strong>
\nRan Zmigrod, Sebastian J. Mielke, Hanna Wallach<\/strong><\/a>, Ryan Cotterell<\/p>\n

16:40 \u2013 17:00 | Session 3B: Word-level Semantics 1
\nUnsupervised Discovery of Gendered Language through Latent-Variable Modeling<\/strong>
\nAlexander Miserlis Hoyle, Lawrence Wolf-Sonkin, Hanna Wallach<\/strong><\/a>, Isabelle Augenstein, Ryan Cotterell<\/p>\n

Tuesday, July 30<\/h3>\n

10:30 \u2013 10:50 | Session 4C: Evaluation
\nSentence Mover’s Similarity: Automatic Evaluation for Multi-Sentence Texts<\/strong>
\nElizabeth Clark, Asli Celikyilmaz<\/a><\/strong>, Noah A. Smith<\/p>\n

10:30 \u2013 12:10 | Multilinguality | Poster Session 4
\nMulti-Source Cross-Lingual Model Transfer: Learning What to Share<\/strong>
\nXilun Chen, Ahmed Hassan Awadallah<\/strong><\/a>, Hany Hassan<\/strong><\/a>, Wei Wang<\/strong><\/a>, Claire Cardie<\/p>\n

10:30 \u2013 12:10 | Word-level Semantics | Poster Session 4
\nEmbedding Imputation with Grounded Language Information<\/strong>
\nZiyi Yang, Chenguang Zhu<\/strong><\/a>, Vin Sachidananda, Eric Darve<\/p>\n

10:30 \u2013 12:10 | Word-level Semantics | Poster Session 4
\nBERT-based Lexical Substitution<\/strong>
\nWangchunshu Zhou<\/strong>, Tao Ge<\/strong>, Ke Xu, Furu Wei<\/strong><\/a>, Ming Zhou<\/strong><\/a><\/p>\n

11:10 \u2013 11:30 | Session 4B: Question Answering 1 – Multi-Hop
\nExplore, Propose, and Assemble: An Interpretable Model for Multi-Hop Reading Comprehension<\/strong>
\nYichen Jiang, Nitish Joshi, Yen-Chun Chen<\/strong>, Mohit Bansal<\/p>\n

11:30 \u2013 11:43 | Session 4A: Dialogue and Generation
\nA Simple Recipe towards Reducing Hallucination in Neural Surface Realisation<\/strong>
\nFeng Nie, Jin-Ge Yao<\/strong>, Jinpeng Wang<\/strong><\/a>, Rong Pan, Chin-Yew Lin<\/strong><\/a><\/p>\n

13:50 \u2013 15:30 | Machine Learning | Poster Session 5
\nTowards Language Agnostic Universal Representations<\/strong>
\nArmen Aghajanyan, Xia Song<\/strong>, Saurabh Tiwary<\/strong><\/p>\n

13:50 \u2013 15:30 | Dialogue and Interactive Systems | Poster Session 5
\nBudgeted Policy Learning for Task-Oriented Dialogue Systems<\/strong>
\nZhirui Zhang, Xiujun Li<\/strong><\/a>, Jianfeng Gao<\/strong><\/a>, Enhong Chen<\/p>\n

13:50 \u2013 15:30 | Dialogue and Interactive Systems | Poster Session 5
\nRetrieval-Enhanced Adversarial Training for Neural Response Generation<\/strong>
\nQingfu Zhu, Lei Cui<\/strong><\/a>, Wei-Nan Zhang, Furu Wei<\/strong><\/a>, Ting Liu<\/p>\n

13:50 \u2013 15:30 | Dialogue and Interactive Systems | Poster Session 5
\nLearning a Matching Model with Co-teaching for Multi-turn Response Selection in Retrieval-based Dialogue Systems<\/strong>
\nJiazhan Feng, Chongyang Tao, Wei Wu<\/strong><\/a>, Yansong Feng, Dongyan Zhao, Rui Yan<\/p>\n

13:50 \u2013 15:30 | Resources and Evaluation | Poster Session 5
\nThe KnowRef Coreference Corpus: Removing Gender and Number Cues for Difficult Pronominal Anaphora Resolution<\/strong>
\nAli Emami, Paul Trichelair, Adam Trischler<\/strong><\/a>, Kaheer Suleman<\/strong><\/a>, Hannes Schulz<\/strong><\/a>, Jackie Chi Kit Cheung<\/p>\n

14:50 \u2013 15:10 | Session 5D: Sentiment Analysis and Argument Mining 2
\nExploring Sequence-to-Sequence Learning in Aspect Term Extraction<\/strong>
\nDehong Ma, Sujian Li, Fangzhao Wu<\/strong><\/a>, Xing Xie<\/strong><\/a>, Houfeng Wang<\/p>\n

Wednesday, July 31<\/h3>\n

10:30 \u2013 12:10 | Sentence-level Semantics | Poster Session 6
\nMulti-Task Deep Neural Networks for Natural Language Understanding<\/strong>
\nXiaodong Liu<\/strong><\/a>, Pengcheng He<\/strong>, Weizhu Chen<\/strong>, Jianfeng Gao<\/strong><\/a><\/p>\n

10:30 \u2013 12:10 | Textual Inference and Other Areas of Semantics | Poster Session 6
\nCOMET: Commonsense Transformers for Automatic Knowledge Graph Construction<\/strong>
\nAntoine Bosselut, Hannah Rashkin, Maarten Sap, Chaitanya Malaviya, Asli Celikyilmaz<\/strong><\/a>, Yejin Choi<\/p>\n

10:30 \u2013 12:10 | Sentence-level Semantics | Poster Session 6
\nTowards Complex Text-to-SQL in Cross-Domain Database with Intermediate Representation<\/strong>
\nJiaqi Guo, Zecheng Zhan, Yan Gao<\/strong>, Yan Xiao<\/strong>, Jian-Guang Lou<\/strong><\/a>, Ting Liu, Dongmei Zhang<\/strong><\/a><\/p>\n

10:50 \u2013 11:10 | Session 6B: Question Answering 2
\nLearning to Ask Unanswerable Questions for Machine Reading Comprehension<\/strong>
\nHaichao Zhu, Li Dong<\/strong>, Furu Wei<\/strong><\/a>, Wenhui Wang<\/strong>, Bing Qin, Ting Liu<\/p>\n

13:50 \u2013 14:10 | Session 7E: Summarization 2
\nHIBERT: Document Level Pre-training of Hierarchical Bidirectional Transformers for Document Summarization<\/strong>
\nXingxing Zhang<\/strong>, Furu Wei<\/strong><\/a>, Ming Zhou<\/strong><\/a><\/p>\n

13:50 \u2013 15:30 | Machine Learning | Poster Session 7
\nSoft Contextual Data Augmentation for Neural Machine Translation<\/strong>
\nJinhua Zhu, Fei Gao, Lijun Wu, Yingce Xia<\/strong><\/a>, Tao Qin<\/strong><\/a>, Wengang Zhou, Xueqi Cheng, Tie-Yan Liu<\/strong><\/a><\/p>\n

13:50 \u2013 15:30 | Information Extraction and Text Mining | Poster Session 7
\nTowards Improving Neural Named Entity Recognition with Gazetteers<\/strong>
\nTianyu Liu, Jin-Ge Yao<\/strong>, Chin-Yew Lin<\/strong><\/a><\/p>\n

13:50 \u2013 15:30 | Information Extraction and Text Mining | Poster Session 7
\nImproving Textual Network Embedding with Global Attention via Optimal Transport<\/strong>
\nLiqun Chen, Guoyin Wang, Chenyang Tao, Dinghan Shen, Pengyu Cheng, Xinyuan Zhang, Wenlin Wang, Yizhe Zhang<\/strong><\/a>, Lawrence Carin<\/p>\n

13:50 \u2013 15:30 | Machine Learning | Poster Session 7
\nDepth Growing for Neural Machine Translation<\/strong>
\nLijun Wu, Yiren Wang, Yingce Xia<\/strong><\/a>, Fei Tian<\/strong>, Fei Gao, Tao Qin<\/strong><\/a>, Jianhuang Lai, Tie-Yan Liu<\/strong><\/a><\/p>\n

13:50 \u2013 15:30 | Dialogue and Interactive Systems | Poster Session 7
\nConversing by Reading: Contentful Neural Conversation with On-demand Machine Reading<\/strong>
\nLianhui Qin, Michel Galley<\/strong><\/a>, Chris Brockett<\/strong><\/a>, Xiaodong Liu<\/strong><\/a>, Xiang Gao<\/strong><\/a>, Bill Dolan<\/strong><\/a>, Yejin Choi, Jianfeng Gao<\/strong><\/a><\/p>\n

13:50 \u2013 15:30 | Dialogue and Interactive Systems | Poster Session 7
\nNeural Response Generation with Meta-words<\/strong>
\nCan Xu<\/strong>, Wei Wu<\/strong><\/a>, Chongyang Tao, Huang Hu<\/strong>, Matt Schuerman<\/strong>, Ying Wang<\/strong><\/p>\n

14:30 \u2013 14:50 | Session 7B: Question Answering 3
\nSimple and Effective Curriculum Pointer-Generator Networks for Reading Comprehension over Long Narratives<\/strong>
\nYi Tay, Shuohang Wang, Anh Tuan Luu, Jie Fu, Minh C. Phan, Xingdi Yuan<\/strong><\/a>, Jinfeng Rao, Siu Cheung Hui, Aston Zhang<\/p>\n

16:00 \u2013 17:40 | Vision, Robotics, Multimodal, Grounding and Speech | Poster Session 8
\nDense Procedure Captioning in Narrated Instructional Videos<\/strong>
\nBotian Shi, Lei Ji<\/strong><\/a>, Yaobo Liang<\/strong><\/a>, Zhendong Niu, Nan Duan<\/strong><\/a>, Ming Zhou<\/strong><\/a><\/p>\n

16:00 \u2013 17:40 | Vision, Robotics, Multimodal, Grounding and Speech | Poster Session 8
\nMulti-step Reasoning via Recurrent Dual Attention for Visual Dialog<\/strong>
\nZhe Gan<\/strong>, Yu Cheng<\/strong>, Ahmed Kholy<\/strong>, Linjie Li<\/strong>, Jingjing Liu<\/strong><\/a>, Jianfeng Gao<\/strong><\/a><\/p>\n

16:00 \u2013 17:40 | Question Answering | Poster Session 8
\nAre Red Roses Red? Evaluating Consistency of Question-Answering Models<\/strong>
\nMarco Tulio Ribeiro<\/strong>, Carlos Guestrin, Sameer Singh<\/p>\n

16:00 \u2013 17:40 | Generation | Poster Session 8
\nAutomatic Grammatical Error Correction for Sequence-to-sequence Text Generation: An Empirical Study<\/strong>
\nTao Ge<\/strong>, Xingxing Zhang<\/strong>, Furu Wei<\/strong><\/a>, Ming Zhou<\/strong><\/a><\/p>\n

16:20 \u2013 16:40 | Session 8C: Resources and Evaluation
\nProbing for Semantic Classes: Diagnosing the Meaning Content of Word Embeddings<\/strong>
\nYadollah Yaghoobzadeh<\/strong>, Katharina Kann, T. J. Hazen<\/strong><\/a>, Eneko Agirre, Hinrich Sch\u00fctze<\/p>\n

16:40 \u2013 17:00 | Session 8A: Dialogue and Interactive Systems 3 – New Tasks
\nTarget-Guided Open-Domain Conversation<\/strong>
\nJianheng Tang, Zhiting Hu, Tiancheng Zhao, Chenyan Xiong<\/strong><\/a>, Xiaodan Liang, Eric Xing<\/p>\n

16:40 \u2013 17:00 | Session 8B: Word-level Semantics 2
\nWord2Sense: Sparse Interpretable Word Embeddings<\/strong>
\nAbhishek Panigrahi<\/strong>, Harsha Vardhan Simhadri, Chiranjib BhattacharyyaOpens in a new tab<\/span><\/p>\n

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Type<\/strong>: Full-time<\/p>

Lab\/Location<\/strong>: Bellevue, Washington<\/p>

Do you like to work in an environment where you can implement new ideas as well as transfer technology from research into the product? You can help us create services that understand what people say and give them the answers that they want and complete the tasks that they need….<\/p>

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Type<\/strong>: Full-time<\/p>

Lab\/Location<\/strong>: Bellevue, Washington<\/p>

Entity Understanding team within Bing search engine is responsible for providing the most relevant answer to user queries related with the real-world entities like people, movies, songs, locations and more. It helps users find the facts about entities quickly without need for searching through web results…<\/p>

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Type<\/strong>: Full-time<\/p>

Lab\/Location<\/strong>: Multiple<\/p>

The Semantic Machines group is working to reshape human-computer interaction with conversational AI in our three offices: Boston MA, Berkeley CA, and Bellevue WA. We’re looking for software engineers to work hand-in-hand with our data and research teams to develop new approaches to solving deep…<\/p>

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Type<\/strong>: Full-time<\/p>

Lab\/Location<\/strong>: Sunnyvale, California<\/p>

What if your job description were simply \u201cMake tomorrow better?\u201d That\u2019s the essence of roles within our Bing Team. Every day, we bring an insatiable curiosity to the table, challenging ourselves to reimagine what is and what can be. We build on what\u2019s come before to create what\u2019s next. We drive machine intelligence. We help…<\/p>

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Type<\/strong>: Full-time<\/p>

Lab\/Location<\/strong>: Sunnyvale, California<\/p>

Online Advertising is one of the fastest growing businesses on the Internet today, with about $70 billion of a $600 billion advertising market already online. Search engines, web publishers, major ad networks, and ad exchanges are now serving billions of ad impressions per day and generating terabytes of user…<\/p>

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Type<\/strong>: Full-time<\/p>

Lab\/Location<\/strong>: Bellevue, Washington<\/p>

BAG AI is working on cutting-edge AI models for business applications. The team is focused on applying advanced natural language processing (NLP) techniques to customer-facing products, such as conversational AI, question answering, entity recognition, and recommendation. We are a hybrid team between research and…<\/p>

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Type<\/strong>: Full-time<\/p>

Lab\/Location<\/strong>: Bellevue, Washington<\/p>

The Bing Search & AI team needs strong PMs to lead a team of engineers and researchers to apply the cutting edge of artificial intelligence and deep learning technologies to solve some challenging business problems for Bing and Microsoft. Specifically, we want to take state of the art natural language understanding…<\/p>

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Type<\/strong>: Full-time<\/p>

Lab\/Location<\/strong>: Redmond, Washington<\/p>

Project EmpowerMD is an incubation team at Microsoft Healthcare. Our mission is to empower physicians by building a voice-enabled intelligent assistant, leveraging the best in speech-to-text, NLP, and other ML technologies. We are a fast-moving multi-disciplinary team that is deeply engaged in the healthcare space with ample…<\/p>

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Type<\/strong>: Full-time<\/p>

Lab\/Location<\/strong>: Multiple<\/p>

Semantic Machines is hiring a few extremely talented researchers to join our world-class team in Boston, MA, Bellevue, WA, and Berkeley, CA. Our team has advanced the state of the art in natural language processing, speech processing, deep learning, video game AI, and even computational historical linguistics. We’re…<\/p>

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Type<\/strong>: Full-time<\/p>

Lab\/Location<\/strong>: Redmond, Washington<\/p>

Artificial intelligence (AI) is dramatically transforming people\u2019s work and life now. Speech technology is in the center of the transform. Speech recognition and text to speech (TTS) are essential pieces to enable intelligent agent in the AI era. Microsoft\u2019s mission is to build human parity TTS voices for as many languages…<\/p>

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Type<\/strong>: Internship<\/p>

Lab\/Location<\/strong>: Redmond, Washington<\/p>

Artificial intelligence (AI) is dramatically transforming people\u2019s work and life now. Speech technology is in the center of the transform. Speech recognition and text to speech (TTS) are essential pieces to enable intelligent agent in the AI era. Microsoft\u2019s mission is to build human parity TTS voices for as many…<\/p>

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Type<\/strong>: Full-time<\/p>

Lab\/Location<\/strong>: Berkeley, California<\/p>

Semantic Machines is hiring a few extremely talented researchers to join our world-class team in Boston, MA, Bellevue, WA, and Berkeley, CA. Our team has advanced the state of the art in natural language processing, speech processing, deep learning, video game AI, and even computational historical linguistics…<\/p>

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Type<\/strong>: Internship<\/p>

Lab\/Location<\/strong>: Redmond, Washington<\/p>

Microsoft provides a nurturing environment to support passionate researchers and engineers in AI technology innovation. We are seeking candidates excelling in deep thinking research aspects and fast-paced entrepreneurial execution…<\/p>

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Type<\/strong>: Full-time<\/p>

Lab\/Location<\/strong>: Redmond, Washington<\/p>

Microsoft provides a nurturing environment to support passionate researchers and engineers in AI technology innovation. We are seeking candidates excelling in deep thinking research aspects and fast-paced entrepreneurial execution…<\/p>

\t\t\t<\/div>\n\t\t<\/div>\n\n\t<\/div>\n<\/article>\n<\/p>\t\t\t<\/div>\n\t\t<\/div>\n\t\tOpens in a new tab<\/span><\/p>\n","tab-content":[{"id":0,"name":"About","content":"Microsoft is excited to be a Diamond sponsor of the<\/a> 57th Annual Meeting of the Association for Computational Linguistics (ACL)<\/a>. We will have over 50 Microsoft attendees present at the conference. Stop by our booth to chat with our experts, see demos of our latest research and find out about career opportunities<\/a>\u00a0with Microsoft.\r\n

Microsoft attendees<\/h3>\r\nAdam Trischler<\/a>\r\nAhmed Awadallah<\/a>\r\nAmitha Gopidi\r\nAndrew McNamara<\/a>\r\nAnoop Kunchukuttan\r\nAsli Celikyilmaz<\/a>\r\nBill Dolan<\/a>\r\nCan Xu\r\nChenguang Zhu<\/a>\r\nChin-Yew Lin<\/a>\r\nDan Klein\r\nDon Graney\r\nDuyu Tang<\/a>\r\nEhsan Zare Borzeshi\r\nEric Lin<\/a>\r\nFitzgerald Nicole\r\nGraney Don\r\nHal Daume III<\/a>\r\nHannes Schulz<\/a>\r\nHany Hassan<\/a>\r\nJianfeng Gao<\/a>\r\nJianguang Lou\r\nJin-Ge Yao\r\nJingjing Liu<\/a>\r\nJinpeng Wang<\/a>\r\nJinxi Xu\r\nKaheer Suleman<\/a>\r\nLei Cui<\/a>\r\nLi Dong\r\nLinjun Shou\r\nMahmoud Adada<\/a>\r\nMark Bolin\r\nMichel Galley<\/a>\r\nMing Gong\r\nMohamad Alkhoujeh\r\nNan Duan<\/a>\r\nPeter Potash<\/a>\r\nRahul Mehrotra<\/a>\r\nSaid Bleik\r\nSamira Shabanian<\/a>\r\nShane Peckham\r\nShuming Ma\r\nSungjin Lee\r\nTao Ge\r\nTao Qin<\/a>\r\nTatsuro Oya<\/a>\r\nTimothy Hazen\r\nVighnesh Shiv\r\nVikas Bahirwani\r\nWenhui Zhang\r\nWenhui Wang\r\nXiang Gao<\/a>\r\nXiaoyu Qi\r\nXihui Lin<\/a>\r\nXingxing Zhang\r\nXiubo Geng\r\nXu Tan<\/a>\r\nYadollah Yaghoobzadeh<\/a>\r\nYingce Xia<\/a>"},{"id":1,"name":"Schedule","content":"

Monday, July 29<\/h3>\r\n10:30 \u2013 10:50 | Session 1A: Dialogue and Interactive Systems 1 - Neural Conversation Models\r\nOne Time of Interaction May Not Be Enough: Go Deep with an Interaction-over-Interaction Network for Response Selection in Dialogues<\/strong>\r\nChongyang Tao, Wei Wu<\/strong><\/a>, Can Xu<\/strong>, Wenpeng Hu, Dongyan Zhao, Rui Yan\r\n\r\n10:30 \u2013 10:50 | Session 1D: Machine Translation 1\r\nUnsupervised Pivot Translation for Distant Languages<\/strong>\r\nYichong Leng, Xu Tan<\/strong><\/a>, Tao Qin<\/strong><\/a>, Xiang-Yang Li, Tie-Yan Liu<\/strong><\/a>\r\n\r\n10:30 \u2013 12:10 | Applications | Poster Session 1\r\nNeural News Recommendation with Long- and Short-term User Representations<\/strong>\r\nMingxiao An, Fangzhao Wu<\/strong><\/a>, Chuhan Wu, Kun Zhang, Zheng Liu<\/strong>, Xing Xie<\/strong><\/a>\r\n\r\n10:30 \u2013 12:10 | Resources and Evaluation | Poster Session 1\r\nErrudite: Scalable, Reproducible, and Testable Error Analysis<\/strong>\r\nTongshuang Wu, Marco Tulio Ribeiro<\/strong>, Jeffrey Heer, Daniel Weld\r\n\r\n11:50 \u2013 12:10 | Session 1B: Sentence-level Semantics\r\nLearning Compressed Sentence Representations for On-Device Text Processing<\/strong>\r\nDinghan Shen, Pengyu Cheng, Dhanasekar Sundararaman, Xinyuan Zhang, Qian Yang, Meng Tang, Asli Celikyilmaz<\/strong><\/a>, Lawrence Carin\r\n\r\n13:50 \u2013 14:10 | Session 2B: Textual Inference and Other Areas of Semantics\r\nCoupling Retrieval and Meta-Learning for Context-Dependent Semantic Parsing<\/strong>\r\nDaya Guo, Duyu Tang<\/strong><\/a>, Nan Duan<\/strong><\/a>, Ming Zhou<\/strong><\/a>, Jian Yin\r\n\r\n13:50 \u2013 15:30 | Information Extraction and Text Mining | Poster Session 2\r\nJoint Type Inference on Entities and Relations via Graph Convolutional Networks<\/strong>\r\nChangzhi Sun, Yeyun Gong<\/strong>, Nan Duan<\/strong><\/a>, Ming Gong<\/strong>, Daxin Jiang<\/strong>, Shiliang Sun, Man Lan, Yuanbin Wu\r\n\r\n13:50 \u2013 15:30 | Applications | Poster Session 2\r\nNeural News Recommendation with Topic-Aware News Representation<\/strong>\r\nChuhan Wu, Fangzhao Wu<\/strong><\/a>, Mingxiao An, Yongfeng Huang, Xing Xie<\/strong><\/a>\r\n\r\n16:00 \u2013 17:40 | Generation | Poster Session 3\r\nData-to-text Generation with Entity Modeling<\/strong>\r\nRatish Puduppully, Li Dong<\/strong>, Mirella Lapata\r\n\r\n16:00 \u2013 17:40 | Generation | Poster Session 3\r\nTowards Generating Long and Coherent Text with Multi-Level Latent Variable Models<\/strong>\r\nDinghan Shen, Asli Celikyilmaz<\/strong><\/a>, Yizhe Zhang<\/strong><\/a>, Liqun Chen, Xin Wang, Jianfeng Gao<\/strong><\/a>, Lawrence Carin\r\n\r\n16:40 \u2013 17:00 | Session 3A: Bias in Language Processing\r\nCounterfactual Data Augmentation for Mitigating Gender Stereotypes in Languages with Rich Morphology<\/strong>\r\nRan Zmigrod, Sebastian J. Mielke, Hanna Wallach<\/strong><\/a>, Ryan Cotterell\r\n\r\n16:40 \u2013 17:00 | Session 3B: Word-level Semantics 1\r\nUnsupervised Discovery of Gendered Language through Latent-Variable Modeling<\/strong>\r\nAlexander Miserlis Hoyle, Lawrence Wolf-Sonkin, Hanna Wallach<\/strong><\/a>, Isabelle Augenstein, Ryan Cotterell\r\n

Tuesday, July 30<\/h3>\r\n10:30 \u2013 10:50 | Session 4C: Evaluation\r\nSentence Mover's Similarity: Automatic Evaluation for Multi-Sentence Texts<\/strong>\r\nElizabeth Clark, Asli Celikyilmaz<\/a><\/strong>, Noah A. Smith\r\n\r\n10:30 \u2013 12:10 | Multilinguality | Poster Session 4\r\nMulti-Source Cross-Lingual Model Transfer: Learning What to Share<\/strong>\r\nXilun Chen, Ahmed Hassan Awadallah<\/strong><\/a>, Hany Hassan<\/strong><\/a>, Wei Wang<\/strong><\/a>, Claire Cardie\r\n\r\n10:30 \u2013 12:10 | Word-level Semantics | Poster Session 4\r\nEmbedding Imputation with Grounded Language Information<\/strong>\r\nZiyi Yang, Chenguang Zhu<\/strong><\/a>, Vin Sachidananda, Eric Darve\r\n\r\n10:30 \u2013 12:10 | Word-level Semantics | Poster Session 4\r\nBERT-based Lexical Substitution<\/strong>\r\nWangchunshu Zhou<\/strong>, Tao Ge<\/strong>, Ke Xu, Furu Wei<\/strong><\/a>, Ming Zhou<\/strong><\/a>\r\n\r\n11:10 \u2013 11:30 | Session 4B: Question Answering 1 - Multi-Hop\r\nExplore, Propose, and Assemble: An Interpretable Model for Multi-Hop Reading Comprehension<\/strong>\r\nYichen Jiang, Nitish Joshi, Yen-Chun Chen<\/strong>, Mohit Bansal\r\n\r\n11:30 \u2013 11:43 | Session 4A: Dialogue and Generation\r\nA Simple Recipe towards Reducing Hallucination in Neural Surface Realisation<\/strong>\r\nFeng Nie, Jin-Ge Yao<\/strong>, Jinpeng Wang<\/strong><\/a>, Rong Pan, Chin-Yew Lin<\/strong><\/a>\r\n\r\n13:50 \u2013 15:30 | Machine Learning | Poster Session 5\r\nTowards Language Agnostic Universal Representations<\/strong>\r\nArmen Aghajanyan, Xia Song<\/strong>, Saurabh Tiwary<\/strong>\r\n\r\n13:50 \u2013 15:30 | Dialogue and Interactive Systems | Poster Session 5\r\nBudgeted Policy Learning for Task-Oriented Dialogue Systems<\/strong>\r\nZhirui Zhang, Xiujun Li<\/strong><\/a>, Jianfeng Gao<\/strong><\/a>, Enhong Chen\r\n\r\n13:50 \u2013 15:30 | Dialogue and Interactive Systems | Poster Session 5\r\nRetrieval-Enhanced Adversarial Training for Neural Response Generation<\/strong>\r\nQingfu Zhu, Lei Cui<\/strong><\/a>, Wei-Nan Zhang, Furu Wei<\/strong><\/a>, Ting Liu\r\n\r\n13:50 \u2013 15:30 | Dialogue and Interactive Systems | Poster Session 5\r\nLearning a Matching Model with Co-teaching for Multi-turn Response Selection in Retrieval-based Dialogue Systems<\/strong>\r\nJiazhan Feng, Chongyang Tao, Wei Wu<\/strong><\/a>, Yansong Feng, Dongyan Zhao, Rui Yan\r\n\r\n13:50 \u2013 15:30 | Resources and Evaluation | Poster Session 5\r\nThe KnowRef Coreference Corpus: Removing Gender and Number Cues for Difficult Pronominal Anaphora Resolution<\/strong>\r\nAli Emami, Paul Trichelair, Adam Trischler<\/strong><\/a>, Kaheer Suleman<\/strong><\/a>, Hannes Schulz<\/strong><\/a>, Jackie Chi Kit Cheung\r\n\r\n14:50 \u2013 15:10 | Session 5D: Sentiment Analysis and Argument Mining 2\r\nExploring Sequence-to-Sequence Learning in Aspect Term Extraction<\/strong>\r\nDehong Ma, Sujian Li, Fangzhao Wu<\/strong><\/a>, Xing Xie<\/strong><\/a>, Houfeng Wang\r\n

Wednesday, July 31<\/h3>\r\n10:30 \u2013 12:10 | Sentence-level Semantics | Poster Session 6\r\nMulti-Task Deep Neural Networks for Natural Language Understanding<\/strong>\r\nXiaodong Liu<\/strong><\/a>, Pengcheng He<\/strong>, Weizhu Chen<\/strong>, Jianfeng Gao<\/strong><\/a>\r\n\r\n10:30 \u2013 12:10 | Textual Inference and Other Areas of Semantics | Poster Session 6\r\nCOMET: Commonsense Transformers for Automatic Knowledge Graph Construction<\/strong>\r\nAntoine Bosselut, Hannah Rashkin, Maarten Sap, Chaitanya Malaviya, Asli Celikyilmaz<\/strong><\/a>, Yejin Choi\r\n\r\n10:30 \u2013 12:10 | Sentence-level Semantics | Poster Session 6\r\nTowards Complex Text-to-SQL in Cross-Domain Database with Intermediate Representation<\/strong>\r\nJiaqi Guo, Zecheng Zhan, Yan Gao<\/strong>, Yan Xiao<\/strong>, Jian-Guang Lou<\/strong><\/a>, Ting Liu, Dongmei Zhang<\/strong><\/a>\r\n\r\n10:50 \u2013 11:10 | Session 6B: Question Answering 2\r\nLearning to Ask Unanswerable Questions for Machine Reading Comprehension<\/strong>\r\nHaichao Zhu, Li Dong<\/strong>, Furu Wei<\/strong><\/a>, Wenhui Wang<\/strong>, Bing Qin, Ting Liu\r\n\r\n13:50 \u2013 14:10 | Session 7E: Summarization 2\r\nHIBERT: Document Level Pre-training of Hierarchical Bidirectional Transformers for Document Summarization<\/strong>\r\nXingxing Zhang<\/strong>, Furu Wei<\/strong><\/a>, Ming Zhou<\/strong><\/a>\r\n\r\n13:50 \u2013 15:30 | Machine Learning | Poster Session 7\r\nSoft Contextual Data Augmentation for Neural Machine Translation<\/strong>\r\nJinhua Zhu, Fei Gao, Lijun Wu, Yingce Xia<\/strong><\/a>, Tao Qin<\/strong><\/a>, Wengang Zhou, Xueqi Cheng, Tie-Yan Liu<\/strong><\/a>\r\n\r\n13:50 \u2013 15:30 | Information Extraction and Text Mining | Poster Session 7\r\nTowards Improving Neural Named Entity Recognition with Gazetteers<\/strong>\r\nTianyu Liu, Jin-Ge Yao<\/strong>, Chin-Yew Lin<\/strong><\/a>\r\n\r\n13:50 \u2013 15:30 | Information Extraction and Text Mining | Poster Session 7\r\nImproving Textual Network Embedding with Global Attention via Optimal Transport<\/strong>\r\nLiqun Chen, Guoyin Wang, Chenyang Tao, Dinghan Shen, Pengyu Cheng, Xinyuan Zhang, Wenlin Wang, Yizhe Zhang<\/strong><\/a>, Lawrence Carin\r\n\r\n13:50 \u2013 15:30 | Machine Learning | Poster Session 7\r\nDepth Growing for Neural Machine Translation<\/strong>\r\nLijun Wu, Yiren Wang, Yingce Xia<\/strong><\/a>, Fei Tian<\/strong>, Fei Gao, Tao Qin<\/strong><\/a>, Jianhuang Lai, Tie-Yan Liu<\/strong><\/a>\r\n\r\n13:50 \u2013 15:30 | Dialogue and Interactive Systems | Poster Session 7\r\nConversing by Reading: Contentful Neural Conversation with On-demand Machine Reading<\/strong>\r\nLianhui Qin, Michel Galley<\/strong><\/a>, Chris Brockett<\/strong><\/a>, Xiaodong Liu<\/strong><\/a>, Xiang Gao<\/strong><\/a>, Bill Dolan<\/strong><\/a>, Yejin Choi, Jianfeng Gao<\/strong><\/a>\r\n\r\n13:50 \u2013 15:30 | Dialogue and Interactive Systems | Poster Session 7\r\nNeural Response Generation with Meta-words<\/strong>\r\nCan Xu<\/strong>, Wei Wu<\/strong><\/a>, Chongyang Tao, Huang Hu<\/strong>, Matt Schuerman<\/strong>, Ying Wang<\/strong>\r\n\r\n14:30 \u2013 14:50 | Session 7B: Question Answering 3\r\nSimple and Effective Curriculum Pointer-Generator Networks for Reading Comprehension over Long Narratives<\/strong>\r\nYi Tay, Shuohang Wang, Anh Tuan Luu, Jie Fu, Minh C. Phan, Xingdi Yuan<\/strong><\/a>, Jinfeng Rao, Siu Cheung Hui, Aston Zhang\r\n\r\n16:00 \u2013 17:40 | Vision, Robotics, Multimodal, Grounding and Speech | Poster Session 8\r\nDense Procedure Captioning in Narrated Instructional Videos<\/strong>\r\nBotian Shi, Lei Ji<\/strong><\/a>, Yaobo Liang<\/strong><\/a>, Zhendong Niu, Nan Duan<\/strong><\/a>, Ming Zhou<\/strong><\/a>\r\n\r\n16:00 \u2013 17:40 | Vision, Robotics, Multimodal, Grounding and Speech | Poster Session 8\r\nMulti-step Reasoning via Recurrent Dual Attention for Visual Dialog<\/strong>\r\nZhe Gan<\/strong>, Yu Cheng<\/strong>, Ahmed Kholy<\/strong>, Linjie Li<\/strong>, Jingjing Liu<\/strong><\/a>, Jianfeng Gao<\/strong><\/a>\r\n\r\n16:00 \u2013 17:40 | Question Answering | Poster Session 8\r\nAre Red Roses Red? Evaluating Consistency of Question-Answering Models<\/strong>\r\nMarco Tulio Ribeiro<\/strong>, Carlos Guestrin, Sameer Singh\r\n\r\n16:00 \u2013 17:40 | Generation | Poster Session 8\r\nAutomatic Grammatical Error Correction for Sequence-to-sequence Text Generation: An Empirical Study<\/strong>\r\nTao Ge<\/strong>, Xingxing Zhang<\/strong>, Furu Wei<\/strong><\/a>, Ming Zhou<\/strong><\/a>\r\n\r\n16:20 \u2013 16:40 | Session 8C: Resources and Evaluation\r\nProbing for Semantic Classes: Diagnosing the Meaning Content of Word Embeddings<\/strong>\r\nYadollah Yaghoobzadeh<\/strong>, Katharina Kann, T. J. Hazen<\/strong><\/a>, Eneko Agirre, Hinrich Sch\u00fctze\r\n\r\n16:40 \u2013 17:00 | Session 8A: Dialogue and Interactive Systems 3 - New Tasks\r\nTarget-Guided Open-Domain Conversation<\/strong>\r\nJianheng Tang, Zhiting Hu, Tiancheng Zhao, Chenyan Xiong<\/strong><\/a>, Xiaodan Liang, Eric Xing\r\n\r\n16:40 \u2013 17:00 | Session 8B: Word-level Semantics 2\r\nWord2Sense: Sparse Interpretable Word Embeddings<\/strong>\r\nAbhishek Panigrahi<\/strong>, Harsha Vardhan Simhadri, Chiranjib Bhattacharyya"},{"id":2,"name":"Career Opportunities","content":"[row]\r\n[card title=\"Applied Researcher\" url=\"https:\/\/careers.microsoft.com\/us\/en\/job\/596990\/Applied-Researcher\" ]\r\n
<\/div>\r\n

Type<\/strong>: Full-time<\/p>\r\n

Lab\/Location<\/strong>: Bellevue, Washington<\/p>\r\n

Do you like to work in an environment where you can implement new ideas as well as transfer technology from research into the product? You can help us create services that understand what people say and give them the answers that they want and complete the tasks that they need....<\/p>\r\n[\/card]\r\n\r\n[card title=\"Software Engineer 2\" url=\"https:\/\/careers.microsoft.com\/us\/en\/job\/642789\/Software-Engineer-2\" ]\r\n

<\/div>\r\n

Type<\/strong>: Full-time<\/p>\r\n

Lab\/Location<\/strong>: Bellevue, Washington<\/p>\r\n

Entity Understanding team within Bing search engine is responsible for providing the most relevant answer to user queries related with the real-world entities like people, movies, songs, locations and more. It helps users find the facts about entities quickly without need for searching through web results...<\/p>\r\n[\/card]\r\n\r\n[card title=\"Software Engineer 2\" url=\"https:\/\/careers.microsoft.com\/us\/en\/job\/642789\/Software-Engineer-2\" ]\r\n

<\/div>\r\n

Type<\/strong>: Full-time<\/p>\r\n

Lab\/Location<\/strong>: Multiple<\/p>\r\n

The Semantic Machines group is working to reshape human-computer interaction with conversational AI in our three offices: Boston MA, Berkeley CA, and Bellevue WA. We're looking for software engineers to work hand-in-hand with our data and research teams to develop new approaches to solving deep...<\/p>\r\n[\/card]\r\n[\/row]\r\n\r\n[row]\r\n[card title=\"Principal Applied Scientist\" url=\"https:\/\/careers.microsoft.com\/us\/en\/job\/641206\/Principal-Applied-Scientist\" ]\r\n

<\/div>\r\n

Type<\/strong>: Full-time<\/p>\r\n

Lab\/Location<\/strong>: Sunnyvale, California<\/p>\r\n

What if your job description were simply \u201cMake tomorrow better?\u201d That\u2019s the essence of roles within our Bing Team. Every day, we bring an insatiable curiosity to the table, challenging ourselves to reimagine what is and what can be. We build on what\u2019s come before to create what\u2019s next. We drive machine intelligence. We help...<\/p>\r\n[\/card]\r\n\r\n[card title=\"Data & Applied Scientist 2\" url=\"https:\/\/careers.microsoft.com\/us\/en\/job\/615637\/Data-Applied-Scientist-2\" ]\r\n

<\/div>\r\n

Type<\/strong>: Full-time<\/p>\r\n

Lab\/Location<\/strong>: Sunnyvale, California<\/p>\r\n

Online Advertising is one of the fastest growing businesses on the Internet today, with about $70 billion of a $600 billion advertising market already online. Search engines, web publishers, major ad networks, and ad exchanges are now serving billions of ad impressions per day and generating terabytes of user...<\/p>\r\n[\/card]\r\n\r\n[card title=\"Senior Data & Applied Scientist\" url=\"https:\/\/careers.microsoft.com\/us\/en\/job\/674734\/Senior-Data-Applied-Scientist\" ]\r\n

<\/div>\r\n

Type<\/strong>: Full-time<\/p>\r\n

Lab\/Location<\/strong>: Bellevue, Washington<\/p>\r\n

BAG AI is working on cutting-edge AI models for business applications. The team is focused on applying advanced natural language processing (NLP) techniques to customer-facing products, such as conversational AI, question answering, entity recognition, and recommendation. We are a hybrid team between research and...<\/p>\r\n[\/card]\r\n[\/row]\r\n\r\n[row]\r\n[card title=\"Senior Program Manager\" url=\"https:\/\/careers.microsoft.com\/us\/en\/job\/674427\/Senior-Program-Manager\" ]\r\n

<\/div>\r\n

Type<\/strong>: Full-time<\/p>\r\n

Lab\/Location<\/strong>: Bellevue, Washington<\/p>\r\n

The Bing Search & AI team needs strong PMs to lead a team of engineers and researchers to apply the cutting edge of artificial intelligence and deep learning technologies to solve some challenging business problems for Bing and Microsoft. Specifically, we want to take state of the art natural language understanding...<\/p>\r\n[\/card]\r\n\r\n[card title=\"Senior Data Scientist\" url=\"https:\/\/careers.microsoft.com\/us\/en\/job\/630193\/Senior-Data-Scientist\" ]\r\n

<\/div>\r\n

Type<\/strong>: Full-time<\/p>\r\n

Lab\/Location<\/strong>: Redmond, Washington<\/p>\r\n

Project EmpowerMD is an incubation team at Microsoft Healthcare. Our mission is to empower physicians by building a voice-enabled intelligent assistant, leveraging the best in speech-to-text, NLP, and other ML technologies. We are a fast-moving multi-disciplinary team that is deeply engaged in the healthcare space with ample...<\/p>\r\n[\/card]\r\n\r\n[card title=\"Principal Researcher\" url=\"https:\/\/careers.microsoft.com\/us\/en\/job\/633065\/Principal-Researcher\" ]\r\n

<\/div>\r\n

Type<\/strong>: Full-time<\/p>\r\n

Lab\/Location<\/strong>: Multiple<\/p>\r\n

Semantic Machines is hiring a few extremely talented researchers to join our world-class team in Boston, MA, Bellevue, WA, and Berkeley, CA. Our team has advanced the state of the art in natural language processing, speech processing, deep learning, video game AI, and even computational historical linguistics. We're...<\/p>\r\n[\/card]\r\n[\/row]\r\n\r\n[row]\r\n[card title=\"Cognition and Speech Scientist\" url=\"https:\/\/careers.microsoft.com\/us\/en\/job\/653143\/Full-Time-Opportunities-for-PhD-Students-or-Recent-Graduates-Cognition-and-Speech-Scientist\" ]\r\n

<\/div>\r\n

Type<\/strong>: Full-time<\/p>\r\n

Lab\/Location<\/strong>: Redmond, Washington<\/p>\r\n

Artificial intelligence (AI) is dramatically transforming people\u2019s work and life now. Speech technology is in the center of the transform. Speech recognition and text to speech (TTS) are essential pieces to enable intelligent agent in the AI era. Microsoft\u2019s mission is to build human parity TTS voices for as many languages...<\/p>\r\n[\/card]\r\n\r\n[card title=\"Cognition and Speech Scientist Internship\" url=\"https:\/\/careers.microsoft.com\/us\/en\/job\/653144\/Internship-Opportunities-for-PhD-Students-Cognition-and-Speech-Scientist\" ]\r\n

<\/div>\r\n

Type<\/strong>: Internship<\/p>\r\n

Lab\/Location<\/strong>: Redmond, Washington<\/p>\r\n

Artificial intelligence (AI) is dramatically transforming people\u2019s work and life now. Speech technology is in the center of the transform. Speech recognition and text to speech (TTS) are essential pieces to enable intelligent agent in the AI era. Microsoft\u2019s mission is to build human parity TTS voices for as many...<\/p>\r\n[\/card]\r\n\r\n[card title=\"Researcher\" url=\"https:\/\/careers.microsoft.com\/us\/en\/job\/619310\/Researcher\" ]\r\n

<\/div>\r\n

Type<\/strong>: Full-time<\/p>\r\n

Lab\/Location<\/strong>: Berkeley, California<\/p>\r\n

Semantic Machines is hiring a few extremely talented researchers to join our world-class team in Boston, MA, Bellevue, WA, and Berkeley, CA. Our team has advanced the state of the art in natural language processing, speech processing, deep learning, video game AI, and even computational historical linguistics...<\/p>\r\n[\/card]\r\n[\/row]\r\n\r\n[row]\r\n[card title=\"PhD Student Researcher\" url=\"https:\/\/careers.microsoft.com\/students\/us\/en\/job\/653133\/Internship-Opportunities-for-PhD-Students-Researcher\" ]\r\n

<\/div>\r\n

Type<\/strong>: Internship<\/p>\r\n

Lab\/Location<\/strong>: Redmond, Washington<\/p>\r\n

Microsoft provides a nurturing environment to support passionate researchers and engineers in AI technology innovation. We are seeking candidates excelling in deep thinking research aspects and fast-paced entrepreneurial execution...<\/p>\r\n[\/card]\r\n\r\n[card title=\"Researcher\" url=\"https:\/\/careers.microsoft.com\/students\/us\/en\/job\/653130\/Full-Time-Opportunities-for-PhD-Students-or-Recent-Graduates-Researcher\" ]\r\n

<\/div>\r\n

Type<\/strong>: Full-time<\/p>\r\n

Lab\/Location<\/strong>: Redmond, Washington<\/p>\r\n

Microsoft provides a nurturing environment to support passionate researchers and engineers in AI technology innovation. We are seeking candidates excelling in deep thinking research aspects and fast-paced entrepreneurial execution...<\/p>\r\n[\/card]\r\n[\/row]"}],"msr_startdate":"2019-07-28","msr_enddate":"2019-08-02","msr_event_time":"","msr_location":"Florence, Italy","msr_event_link":"","msr_event_recording_link":"","msr_startdate_formatted":"July 28, 2019","msr_register_text":"Watch now","msr_cta_link":"","msr_cta_text":"","msr_cta_bi_name":"","featured_image_thumbnail":"\"Microsoft","event_excerpt":"Microsoft is excited to be a Diamond sponsor of the 57th Annual Meeting of the Association for Computational Linguistics (ACL). We will have over 50 Microsoft attendees present at the conference. Stop by our booth to chat with our experts, see demos of our latest research and find out about career opportunities\u00a0with Microsoft. Microsoft attendees Adam Trischler Ahmed Awadallah Amitha Gopidi Andrew McNamara Anoop Kunchukuttan Asli Celikyilmaz Bill Dolan Can Xu Chenguang Zhu Chin-Yew Lin…","msr_research_lab":[],"related-researchers":[],"msr_impact_theme":[],"related-academic-programs":[],"related-groups":[],"related-projects":[],"related-opportunities":[],"related-publications":[629295],"related-videos":[],"related-posts":[598369,599043,599313],"_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-event\/599466","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-event"}],"about":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/types\/msr-event"}],"version-history":[{"count":6,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-event\/599466\/revisions"}],"predecessor-version":[{"id":1147035,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-event\/599466\/revisions\/1147035"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media\/599481"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=599466"}],"wp:term":[{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=599466"},{"taxonomy":"msr-region","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-region?post=599466"},{"taxonomy":"msr-event-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-event-type?post=599466"},{"taxonomy":"msr-video-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video-type?post=599466"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=599466"},{"taxonomy":"msr-program-audience","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-program-audience?post=599466"},{"taxonomy":"msr-post-option","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-post-option?post=599466"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=599466"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}