Evidence Retrieval is almost All You Need for Fact Verification (2024.findings-acl)
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| Challenge: | Existing evidence retrieval methods adopt a trivial retrieval strategy, resulting in task-irrelevant evidence and undesirable performance. |
| Approach: | They propose a framework for evidence retrieval and joint fact verification that integrates two modules. |
| Outcome: | The proposed framework improves evidence retrieval and claims verification on a FEVER dataset. |
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| Challenge: | Existing evidence retrieval methods are limited by single-stage evidence extraction. |
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| Challenge: | Existing methods for fact verification rely on extracted evidence, but there is little work on understanding the reasoning process. |
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| Challenge: | Prior work makes simplifying assumptions in retrieval that depart from real-world use cases: no access to evidence, access to curated evidence, or access to published evidence after a claim was made. |
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| Challenge: | Existing methods for fact extraction and verification combine all evidence sentences to produce redundant information. |
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| Challenge: | Automated fact-checking on a large scale is time consuming and intractable. |
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Natural Logic-guided Autoregressive Multi-hop Document Retrieval for Fact Verification (2022.emnlp-main)
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| Challenge: | Recent evidence retrieval approaches rely on heuristics and assume hyperlinks between documents. |
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