QAConv: Question Answering on Informative Conversations

• URL: http://arxiv.org/abs/2105.06912v1
• Date: Fri, 14 May 2021 15:53:05 GMT
• Title: QAConv: Question Answering on Informative Conversations
• Authors: Chien-Sheng Wu, Andrea Madotto, Wenhao Liu, Pascale Fung, Caiming Xiong
• Abstract summary: We focus on informative conversations including business emails, panel discussions, and work channels. In total, we collect 34,204 QA pairs, including span-based, free-form, and unanswerable questions.
• Score: 85.2923607672282
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper introduces QAConv, a new question answering (QA) dataset that uses conversations as a knowledge source. We focus on informative conversations including business emails, panel discussions, and work channels. Unlike open-domain and task-oriented dialogues, these conversations are usually long, complex, asynchronous, and involve strong domain knowledge. In total, we collect 34,204 QA pairs, including span-based, free-form, and unanswerable questions, from 10,259 selected conversations with both human-written and machine-generated questions. We segment long conversations into chunks, and use a question generator and dialogue summarizer as auxiliary tools to collect multi-hop questions. The dataset has two testing scenarios, chunk mode and full mode, depending on whether the grounded chunk is provided or retrieved from a large conversational pool. Experimental results show that state-of-the-art QA systems trained on existing QA datasets have limited zero-shot ability and tend to predict our questions as unanswerable. Fine-tuning such systems on our corpus can achieve significant improvement up to 23.6% and 13.6% in both chunk mode and full mode, respectively.

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