Neural Argument Generation Augmented with Externally Retrieved Evidence
Xinyu Hua, Lu Wang · 2018
High quality arguments are essential elements for human reasoning and decision-making processes.However, effective 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 topicrelevant content than a popular sequence-tosequence generation model according to both automatic evaluation and human assessments.