{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/190409380","title":"Repurposing Entailment for Multi-Hop Question Answering Tasks","arxiv_id":"1904.09380","date":"2019-04-20","proceeding":"NAACL 2019 6","authors":["Harsh Trivedi","Heeyoung Kwon","Tushar Khot","Ashish Sabharwal","Niranjan Balasubramanian"],"abstract":"Question Answering (QA) naturally reduces to an entailment problem, namely,\nverifying whether some text entails the answer to a question. However, for\nmulti-hop QA tasks, which require reasoning with multiple sentences, it remains\nunclear how best to utilize entailment models pre-trained on large scale\ndatasets such as SNLI, which are based on sentence pairs. We introduce Multee,\na general architecture that can effectively use entailment models for multi-hop\nQA tasks. Multee uses (i) a local module that helps locate important sentences,\nthereby avoiding distracting information, and (ii) a global module that\naggregates information by effectively incorporating importance weights.\nImportantly, we show that both modules can use entailment functions pre-trained\non a large scale NLI datasets. We evaluate performance on MultiRC and\nOpenBookQA, two multihop QA datasets. When using an entailment function\npre-trained on NLI datasets, Multee outperforms QA models trained only on the\ntarget QA datasets and the OpenAI transformer models. The code is available at\nhttps://github.com/StonyBrookNLP/multee.","url_abs":"http://arxiv.org/abs/1904.09380v1","url_pdf":"http://arxiv.org/pdf/1904.09380v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"190409380","repo_url":"https://github.com/StonyBrookNLP/multee","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null},{"paper_slug":"190409380","repo_url":"https://github.com/soujanyarbhat/aNswER_multirc","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"190409380","repo_url":"https://github.com/varunchaudharycs/aNswER_multirc","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"190409380","repo_url":"https://github.com/varunchaudharycs/multi_rc","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"multi-hop-question-answering","task_name":"Multi-hop Question Answering"},{"task_slug":"question-answering","task_name":"Question Answering"},{"task_slug":"sentence","task_name":"Sentence"}],"methods":[{"method_slug":"absolute-position-encodings","method_name":"Absolute Position Encodings"},{"method_slug":"adam","method_name":"Adam"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"bpe","method_name":"BPE"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"label-smoothing","method_name":"Label Smoothing"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"position-wise-feed-forward-layer","method_name":"Position-Wise Feed-Forward Layer"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"transformer","method_name":"Transformer"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1904.09380","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}