Neural Argument Generation Augmented with Externally Retrieved Evidence

ACL 2018

Neural Argument Generation Augmented with Externally Retrieved Evidence

Jan 27, 2021
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Abstract: High quality arguments are essential elements for human reasoning and decision-making processes. However, effec-tive argument construction is a challenging task for both human and machines. In this work, we study a novel task on automatically generating arguments of a different stance for a given statement. We propose an encoder-decoder style neural network-based argument generation model enriched with externally retrieved evidence from Wikipedia. Our model first generates a set of talking point phrases as intermediate representation, followed by a separate decoder producing the final argument based on both input and the keyphrases. Experiments on a large-scale dataset collected from Reddit show that our model constructs arguments with more topic-relevant content than popular sequence-to-sequence generation models according to automatic evaluation and human assessments. Authors: Xinyu Hua, Lu Wang (Northeastern University)

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