{"id":944574,"date":"2023-05-26T10:47:46","date_gmt":"2023-05-26T17:47:46","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/"},"modified":"2023-06-19T10:17:39","modified_gmt":"2023-06-19T17:17:39","slug":"task-aware-specialization-for-efficient-and-robust-dense-retrieval-for-open-domain-question-answering","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/task-aware-specialization-for-efficient-and-robust-dense-retrieval-for-open-domain-question-answering\/","title":{"rendered":"Task-Aware Specialization for Efficient and Robust Dense Retrieval for Open-Domain Question Answering"},"content":{"rendered":"<p>Given its effectiveness on knowledge-intensive natural language processing tasks, dense retrieval models have become increasingly popular. Specifically, the de-facto architecture for open-domain question answering uses two isomorphic encoders that are initialized from the same pretrained model but separately parameterized for questions and passages. This bi-encoder architecture is parameter-inefficient in that there is no parameter sharing between encoders. Further, recent studies show that such dense retrievers underperform BM25 in various settings. We thus propose a new architecture, Task-aware Specialization for dense Retrieval (TASER), which enables parameter sharing by interleaving shared and specialized blocks in a single encoder. Our experiments on five question answering datasets show that TASER can achieve superior accuracy, surpassing BM25, while using about 60% of the parameters as bi-encoder dense retrievers. In out-of-domain evaluations, TASER is also empirically more robust than bi-encoder dense retrievers. Our code is available at\u00a0<a class=\"link-external link-https\" href=\"https:\/\/github.com\/microsoft\/taser\" rel=\"external noopener nofollow\">https:\/\/github.com\/microsoft\/taser<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Given its effectiveness on knowledge-intensive natural language processing tasks, dense retrieval models have become increasingly popular. Specifically, the de-facto architecture for open-domain question answering uses two isomorphic encoders that are initialized from the same pretrained model but separately parameterized for questions and passages. This bi-encoder architecture is parameter-inefficient in that there is no parameter sharing [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":[{"type":"user_nicename","value":"Hao Cheng","user_id":"39922"},{"type":"user_nicename","value":"Hao Fang","user_id":"39465"},{"type":"user_nicename","value":"Xiaodong Liu","user_id":"34877"},{"type":"user_nicename","value":"Jianfeng Gao","user_id":"32246"}],"msr_publishername":"","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"","msr_number":"","msr_organization":"","msr_pages_string":"","msr_page_range_start":"","msr_page_range_end":"","msr_series":"","msr_volume":"","msr_copyright":"","msr_conference_name":"ACL 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