| Challenge: | a system that finds the strongest supporting evidence for a given answer is proposed . a study using passage-based question-answering (QA) shows that agents select evidence that generalizes . |
| Approach: | They propose a system that finds the strongest supporting evidence for a given answer . they use passage-based question-answering (QA) as a testbed to train evidence agents . |
| Outcome: | The proposed system improves QA in a robust manner by using agent-selected evidence. |
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Can NLI Models Verify QA Systems’ Predictions? (2021.findings-emnlp)
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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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Learning to Generalize for Cross-domain QA (2023.findings-acl)
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| Challenge: | Existing methods for QA are hampered by increased training costs . current methods suffer significant performance degradation when applied to out-of-domain examples. |
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Complementary Evidence Identification in Open-Domain Question Answering (2021.eacl-main)
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| Challenge: | Existing approaches to QA that only measure the relevance between the question and each paragraph are not effective. |
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Generalizing Question Answering System with Pre-trained Language Model Fine-tuning (D19-58)
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| Challenge: | Existing methods focus on improving in-domain performance, leaving open the question of how they can generalize to out-of-domain and unseen RC tasks. |
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| Challenge: | Recent explosion of question-answering datasets and models has increased interest in generalization of models across multiple domains and formats. |
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QA-NatVer: Question Answering for Natural Logic-based Fact Verification (2023.emnlp-main)
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Question Answering Infused Pre-training of General-Purpose Contextualized Representations (2022.findings-acl)
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UNIFIEDQA: Crossing Format Boundaries with a Single QA System (2020.findings-emnlp)
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Daniel Khashabi, Sewon Min, Tushar Khot, Ashish Sabharwal, Oyvind Tafjord, Peter Clark, Hannaneh Hajishirzi
| Challenge: | Question answering (QA) tasks have been posed using a variety of formats . a new study aims to develop specialized QA models that can be used to train QA systems . |
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Long Context Question Answering via Supervised Contrastive Learning (2022.naacl-main)
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| Challenge: | Long-context question answering tasks often require identifying evidence spans (e.g., sentences) prior work showed that jointly training models to perform evidence extraction and question answering is important for achieving high performance. |
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