Quick and (not so) Dirty: Unsupervised Selection of Justification Sentences for Multi-hop Question Answering (D19-1)
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| Challenge: | Arras et al., 2017) suggest an unsupervised strategy for the selection of justification sentences for multi-hop question answering (QA) . |
| Approach: | They propose an unsupervised strategy for the selection of justification sentences for multi-hop question answering that maximizes the relevance of the selected sentences, minimizes overlap between selected facts, and maximizes coverage of both question and answer. |
| Outcome: | The proposed strategy improves state-of-the-art supervised QA model on two multi-hop QA datasets: AI2’s Reasoning Challenge (ARC) and Multi-Sentence Reading Comprehension (MultiRC). |
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| Challenge: | et al. : evidence retrieval is highly dependent on partial, incorrect or no supporting knowledge. |
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Dheeru Dua, Cicero Nogueira dos Santos, Patrick Ng, Ben Athiwaratkun, Bing Xiang, Matt Gardner, Sameer Singh
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| Challenge: | In multi-hop question answering, models need to connect multiple pieces of evidence scattered in a long context to answer the question. |
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| Challenge: | Existing evaluations of multi-hop question answering systems focus on comparing final answers of reasoning method and given ground-truths. |
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