Generating Sentential Arguments from Diverse Perspectives on Controversial Topic
ChaeHun Park, Wonsuk Yang, Jong Cheol Park · 2019
Considering diverse aspects of an argumentative issue is an essential step for mitigating a biased opinion and making reasonable decisions.A related generation model can produce flexible results that cover a wide range of topics, compared to the retrieval-based method that may show unstable performance for unseen data.In this paper, we study the problem of generating sentential arguments from multiple perspectives, and propose a neural method to address this problem.Our model, ArgDiver (Argument generation model from Diverse perspectives), in a way a conversational system, successfully generates high-quality sentential arguments.At the same time, the automatically generated arguments by our model show a higher diversity than those generated by any other baseline models.We believe that our work provides evidence for the potential of a good generation model in providing diverse perspectives on a controversial topic.