Portrait of Chris Quirk

Chris Quirk

Principal Researcher

About

After studying Computer Science and Mathematics at Carnegie Mellon University, I joined Microsoft in 2000 to work on the Intentional Programming project, an extensible compiler and development framework. I moved to the Natural Language Processing group in 2001, where my research has mostly focused on statistical machine translation powering Microsoft Translator, especially on several generations of a syntax directed translation system that powers over half of the translation systems. I am also interested in semantic parsing, paraphrase methods, and very practical problems such as spelling correction and transliteration.

Projects

OPAL

Established: April 26, 2016

OPAL is a programming language and environment for designing intelligent assistants based on natural language. Tasks in OPAL can explore multiple hypothetical worlds to resolve the ambiguity in users' intent by exploring its potential implications on the real world. Weightings let programs search for the most plausible hypothetical world.

Language to Code

Established: May 12, 2015

Our goal is to let normal users tell computers what to do using normal language. This problem space is strongly related to natural language understanding, program synthesis, and many other areas. The data release associated with the following ACL publication LANGUAGE TO CODE: LEARNING SEMANTIC PARSERS FOR IF-THIS-THEN-THAT RECIPES Chris Quirk, Raymond Mooney and Michel Galley is available here: https://www.microsoft.com/en-us/download/details.aspx?id=52326

NLPwin parses AMR

Established: March 17, 2015

The Logical Form analysis produced by the NLPwin parser is very close in spirit to the level of semantic representation defined in AMR, Abstract Meaning Representation. The "NLPwin parses AMR" project is a conversion from LF to AMR in order to facilitate 1) evaluation of the NLPwin LF and 2) contribution the ongoing discussion of the specification of AMR. In this project, we include publications, as well as links to our LF training data converted…

Data-Driven Conversation

Established: June 1, 2014

This project aims to enable people to converse with their devices. We are trying to teach devices to engage with humans using human language in ways that appear seamless and natural to humans. Our research focuses on statistical methods by which devices can learn from human-human conversational interactions and can situate responses in the verbal context and in physical or virtual environments. Natural and Engaging Agents that process human language will play a growing role…

Recurrent Neural Networks for Language Processing

Established: November 23, 2012

This project focuses on advancing the state-of-the-art in language processing with recurrent neural networks. We are currently applying these to language modeling, machine translation, speech recognition, language understanding and meaning representation. A special interest in is adding side-channels of information as input, to model phenomena which are not easily handled in other frameworks. A toolkit for doing RNN language modeling with side-information is in the associated download. Sample word vectors for use with this toolkit…

MSR SPLAT

Established: April 4, 2012

Statistical Parsing and Linguistic Analysis Toolkit is a linguistic analysis toolkit. Its main goal is to allow easy access to the linguistic analysis tools produced by the Natural Language Processing group at Microsoft Research. The tools include both traditional linguistic analysis tools such as part-of-speech taggers and parsers, and more recent developments, such as sentiment analysis (identifying whether a particular of text has positive or negative sentiment towards its focus) Demo URL: You can find…

Projects

Link description

UW/MS symposium

Date

June 6, 2008

Speakers

Danyel Fisher, Douglas Downey, Chris Quirk, Scott Drellishak, Kelly O'Hara, Emily M. Bender, Sumit Basu, Matthew Hurst, Arnd Christian König, Michael Gamon, Chris Brockett, Dmitriy Belenko, Bill Dolan, Jianfeng Gao, and Lucy Vanderwende

Other

I mostly work with folks in the NLP and Machine Translation groups.

I’ve had the privilege of mentoring or working with a number of interns over the years, including Katharina Probst, Colin Cherry, Pavel Pecina, Ethan Phelps-Goodman, Vivek Srikumar, Arne Mauser, Hao Zhang, Jason Smith, Mohit Bansal, Arianna Bisazza, Mayank Srivastava, Jenny Lin, Joern Wuebker, Juri Ganitkevich, Hui Zhang, and Wei Deng.

Apply for internships at MSR — it’s a fantastic program!

I just came back from a Summer Workshop at Johns Hopkins working on Domain Adaptation for Statistical Machine Translation.

Teaching

Academic Service

Reviewer for ACL, EMNLP, COLING, MT Summit consistently over the past 5+ years. Area Chair in MT: ACL 2009, EMNLP 2009, 2012