Complementary Evidence Identification in Open-Domain Question Answering
| Autor: | Mo Yu, Hui Su, Shiyu Chang, Li Zhang, Yufei Feng, Xiangyang Mou |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Computation and Language Computer science business.industry Complex question computer.software_genre Small set Domain (software engineering) Identification (information) Selection (linguistics) Question answering Relevance (information retrieval) Artificial intelligence Set (psychology) business computer Computation and Language (cs.CL) Natural language processing |
| Zdroj: | EACL |
| Popis: | This paper proposes a new problem of complementary evidence identification for open-domain question answering (QA). The problem aims to efficiently find a small set of passages that covers full evidence from multiple aspects as to answer a complex question. To this end, we proposes a method that learns vector representations of passages and models the sufficiency and diversity within the selected set, in addition to the relevance between the question and passages. Our experiments demonstrate that our method considers the dependence within the supporting evidence and significantly improves the accuracy of complementary evidence selection in QA domain. 7 pages, EACL 2021 |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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