  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.
