2018
EMNLP
EMNLP 2018
A dataset and baselines for sequential open-domain question answering
Abstract
AbstractPrevious work on question-answering systems mainly focuses on answering individual questions, assuming they are independent and devoid of context. Instead, we investigate sequential question answering, asking multiple related questions. We present QBLink, a new dataset of fully human-authored questions. We extend existing strong question answering frameworks to include previous questions to improve the overall question-answering accuracy in open-domain question answering. The dataset is publicly available at http://sequential.qanta.org.
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Interdisciplinary Bridge
— Machine Learning and Natural Language Processing
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Hot Topic Early Bird
— open-domain question answering
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Security & Privacy, Speech & Audio