Probabilistic Assumptions Matter: Improved Models for Distantly-Supervised Document-Level Question Answering (2020.acl-main)
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| Challenge: | Distant supervision assumptions have enabled the creation of large-scale extractive short answer question answering systems. |
| Approach: | They propose to use document-level distant supervision assumptions to pair questions and relevant documents with answer strings. |
| Outcome: | The proposed model outperforms state-of-the-art models by 4.3 points on TriviaQA-Wiki and 1.7 points on NarrativeQA summaries. |
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| Challenge: | Recent question answering systems perform well on benchmark datasets, but are not always well-calibrated to spot spurious answers under distribution shifts. |
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| Challenge: | Recent advances in dense neural retrievers and language models have hindered performance, especially for less common entities and facts. |
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| Challenge: | Open-domain question answering uses evidence retrieved from large corpus to answer questions . state-of-the-art approaches require intermediate evidence annotations for training . however, such intermediate annotations are expensive and methods that rely on them cannot transfer to the more common setting . |
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| Challenge: | a workshop focuses on machine reading for question answering . despite recent progress, there is much to be desired about these datasets and systems . |
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