@inproceedings{park-etal-2019-generating,
title = "Generating Sentential Arguments from Diverse Perspectives on Controversial Topic",
author = "Park, ChaeHun and
Yang, Wonsuk and
Park, Jong",
editor = "Feldman, Anna and
Da San Martino, Giovanni and
Barr{\'o}n-Cede{\~n}o, Alberto and
Brew, Chris and
Leberknight, Chris and
Nakov, Preslav",
booktitle = "Proceedings of the Second Workshop on Natural Language Processing for Internet Freedom: Censorship, Disinformation, and Propaganda",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://preview.aclanthology.org/Add-Cong-Liu-Florida-Atlantic-University-author-id/D19-5007/",
doi = "10.18653/v1/D19-5007",
pages = "56--65",
abstract = "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."
}
Markdown (Informal)
[Generating Sentential Arguments from Diverse Perspectives on Controversial Topic](https://preview.aclanthology.org/Add-Cong-Liu-Florida-Atlantic-University-author-id/D19-5007/) (Park et al., NLP4IF 2019)
ACL