When Retriever-Reader Meets Scenario-Based Multiple-Choice Questions

• URL: http://arxiv.org/abs/2108.13875v1
• Date: Tue, 31 Aug 2021 14:32:04 GMT
• Title: When Retriever-Reader Meets Scenario-Based Multiple-Choice Questions
• Authors: Zixian Huang, Ao Wu, Yulin Shen, Gong Cheng, Yuzhong Qu
• Abstract summary: We propose a joint retriever-reader model called QAVES where the retriever is implicitly supervised only using relevance labels via a novel word weighting mechanism. QAVES significantly outperforms a variety of strong baselines on multiple-choice questions in three SQA datasets.
• Score: 15.528174963480614
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Scenario-based question answering (SQA) requires retrieving and reading paragraphs from a large corpus to answer a question which is contextualized by a long scenario description. Since a scenario contains both keyphrases for retrieval and much noise, retrieval for SQA is extremely difficult. Moreover, it can hardly be supervised due to the lack of relevance labels of paragraphs for SQA. To meet the challenge, in this paper we propose a joint retriever-reader model called JEEVES where the retriever is implicitly supervised only using QA labels via a novel word weighting mechanism. JEEVES significantly outperforms a variety of strong baselines on multiple-choice questions in three SQA datasets.

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