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.
This project aims to break new ground in the burgeoning area of argument mining. It develops a collection of computational models that comprise the basis of an argumentation toolkit---methods that can be combined and reused to support a range of argumentation applications. The project focuses on inter-related threads of research covering three critical areas of exploration for computational argumentation: (1) argument extraction---making sense of argumentative text. Drawing upon recent developments in structured learning, techniques are developed to identify the components and the structure of an argument within a single document or single turn in an online dialog. (2) Argument aggregation---clustering the components of argumentative text (e.g. sentences, turns) drawn from multiple documents according to the facets of the topic under discussion that they address. Representation learning methods are proposed to better capture topical content and argumentative styles. (3) Argument generation---constructing coherent arguments via rewriting. A neural argument generation framework with key phrase extraction as an intermediate representation is created to improve interpretation of sentences from different sources. A discourse-aware neural generation model is also investigated as an extension to improve the coherence of the generated text.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
