Neural Argument Generation Augmented with Externally Retrieved Evidence
This addresses the challenge of effective argument construction for AI systems, though it is incremental as it builds on existing neural generation methods with external evidence.
The paper tackled the problem of automatically generating arguments of a different stance for a given statement by proposing a neural network model augmented with externally retrieved evidence from Wikipedia. The result showed that the model produced arguments with more topic-relevant content than a sequence-to-sequence baseline, as confirmed by automatic and human evaluations on a Reddit dataset.
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 topic-relevant content than a popular sequence-to-sequence generation model according to both automatic evaluation and human assessments.