Context-Aware Answer Extraction in Question Answering

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dc.contributor.authorSeonwoo, Yeonko
dc.contributor.authorKim, Ji-Hoonko
dc.contributor.authorHa, Jung-Wooko
dc.contributor.authorOh, Alice Haeyunko
dc.identifier.citationThe 2020 Conference on Empirical Methods in Natural Language Processing, pp.2418 - 2428-
dc.description.abstractExtractive QA models have shown very promising performance in predicting the correct answer to a question for a given passage. However, they sometimes result in predicting the correct answer text but in a context irrelevant to the given question. This discrepancy becomes especially important as the number of occurrences of the answer text in a passage increases. To resolve this issue, we propose BLANC (BLock AttentioN for Context prediction) based on two main ideas: context prediction as an auxiliary task in multi-task learning manner, and a block attention method that learns the context prediction task. With experiments on reading comprehension, we show that BLANC outperforms the state-ofthe-art QA models, and the performance gap increases as the number of answer text occurrences increases. We also conduct an experiment of training the models using SQuAD and predicting the supporting facts on HotpotQA and show that BLANC outperforms all baseline models in this zero-shot setting.-
dc.publisherAssociation for Computational Linguistics-
dc.titleContext-Aware Answer Extraction in Question Answering-
dc.citation.publicationnameThe 2020 Conference on Empirical Methods in Natural Language Processing-
dc.contributor.localauthorOh, Alice Haeyun-
dc.contributor.nonIdAuthorKim, Ji-Hoon-
dc.contributor.nonIdAuthorHa, Jung-Woo-
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CS-Conference Papers(학술회의논문)
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