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Quick and (not so) Dirty: Unsupervised Selection of Justification Sentences for Multi-hop Question Answering

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arxiv 1911.07176 v2 pith:F5WWEG6J submitted 2019-11-17 cs.CL

Vikas Yadav , Steven Bethard , Mihai Surdeanu This is my paper · ORCID
classification cs.CL
keywords sentencesselectionjustificationselectedunsupervisedmethodmulti-hopmultirc
verification ladder T0 review T1 audit T2 compute T3 formal
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We propose an unsupervised strategy for the selection of justification sentences for multi-hop question answering (QA) that (a) maximizes the relevance of the selected sentences, (b) minimizes the overlap between the selected facts, and (c) maximizes the coverage of both question and answer. This unsupervised sentence selection method can be coupled with any supervised QA approach. We show that the sentences selected by our method improve the performance of a state-of-the-art supervised QA model on two multi-hop QA datasets: AI2's Reasoning Challenge (ARC) and Multi-Sentence Reading Comprehension (MultiRC). We obtain new state-of-the-art performance on both datasets among approaches that do not use external resources for training the QA system: 56.82% F1 on ARC (41.24% on Challenge and 64.49% on Easy) and 26.1% EM0 on MultiRC. Our justification sentences have higher quality than the justifications selected by a strong information retrieval baseline, e.g., by 5.4% F1 in MultiRC. We also show that our unsupervised selection of justification sentences is more stable across domains than a state-of-the-art supervised sentence selection method.

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Cited by 4 Pith papers

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