Understanding, evaluating and generating arguments are all crucial elements in the decision-making and reasoning process. Not surprisingly then, a multitude of arguments are encountered and constructed on a daily basis as decisions are made at work and at home, in our social life and in our civic life. In spite of their ubiquity in our lives, most people are not particularly skilled in the interpretation or generation of arguments. At best, making sense of the often massive amount of argumentative online text on a topic of interest remains a daunting task. And while numerous tools exist for representing, modeling and visualizing arguments and argumentative discussions, they are limited by the substantial human effort required to input, organize and annotate arguments for use by the tools. Thus there exists a pressing need for, and this project aims to develop, automated techniques from the field of Natural Language Processing to support all facets of argumentation. This project will have a wide array of broader impacts, including providing other researchers with annotated datasets and tools for the analysis and generation of arguments, enhancing education through graduate and undergraduate mentoring, and promoting STEM education diversity through programs for middle and high school girls.
Argument mining, argument generation, text generation, discourse analysis
NSF, Award Number: 1813341. Duration: August 1, 2018 - August 30, 2020.
Continued as 2100885 at UM. Duration: August 31, 2020 - July 31, 2021 (extended to July 31, 2022).
Efficient Argument Structure Extraction with Transfer Learning and Active Learning. Xinyu Hua and Lu Wang. Findings of the Association for Computational Linguistics (Findings of ACL), 2022.
DYPLOC: Dynamic Planning of Content Using Mixed Language Models for Text Generation. Xinyu Hua, Ashwin Sreevatsa, and Lu Wang. Proceedings of Annual Meeting of the Association for Computational Linguistics (ACL), 2021.
PAIR: Planning and Iterative Refinement in Pre-trained Transformers for Long Text Generation. Xinyu Hua and Lu Wang. Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 2020.
Sentence-Level Content Planning and Style Specification for Neural Text Generation. Xinyu Hua and Lu Wang. Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 2019.
Argument Generation with Retrieval, Planning, and Realization. Xinyu Hua, Zhe Hu, and Lu Wang. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL), 2019.
Argument Mining for Understanding Peer Reviews. Xinyu Hua, Mitko Nikolov, Nikhil Badugu, and Lu Wang. Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), short paper, 2019.
Neural Argument Generation Augmented with Externally Retrieved Evidence. Xinyu Hua and Lu Wang. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (ACL), 2018.