RI: Small: Collaborative Research: Computational Methods for Argument Mining: Extraction, Aggregation, and Generation

RI：小型：协作研究：参数挖掘的计算方法：提取、聚合和生成

基本信息

批准号：
2100885
负责人：
Lu Wang
金额：
$ 16.05万
依托单位：
Regents of the University of Michigan - Ann Arbor
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-08-31 至 2022-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2100885&HistoricalAwards=false
关键词：
RI Small Collaborative Research Computational

项目摘要

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 otherresearchers 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.

理解、评估和产生论点都是决策和推理过程中的关键要素。因此，当我们在工作、家庭、社交生活和公民生活中做出决策时，每天都会遇到并构建大量的争论，这并不奇怪。尽管它们在我们的生活中无处不在，但大多数人并不特别擅长解释或生成论证。充其量，理解有关感兴趣主题的大量争论性在线文本仍然是一项艰巨的任务。尽管存在许多用于表示、建模和可视化论证和论证性讨论的工具，但它们受到输入、组织和注释工具使用的论证所需的大量人力的限制。因此，迫切需要自然语言处理领域的自动化技术来支持论证的各个方面，该项目旨在开发自动化技术。该项目将产生更广泛的影响，包括为其他研究人员提供带注释的数据集和工具来分析和生成论点，通过研究生和本科生指导加强教育，以及通过针对初中和高中女生的项目促进 STEM 教育多样性。该项目旨在在论证挖掘这一新兴领域开辟新天地。它开发了一系列计算模型，这些模型构成了论证工具包的基础——可以组合和重用以支持一系列论证应用的方法。该项目重点关注相互关联的研究线索，涵盖计算论证探索的三个关键领域：（1）论证提取——理解论证文本。利用结构化学习的最新发展，开发了一些技术来识别单个文档或在线对话中的单个回合中的论点的组成部分和结构。 (2) 论证聚合——根据所讨论的主题的各个方面，对从多个文档中提取的论证文本的组成部分（例如句子、转折）进行聚类。提出表征学习方法是为了更好地捕捉主题内容和论证风格。 (3)论证生成——通过重写构建连贯的论证。创建了一个以关键短语提取作为中间表示的神经论证生成框架，以改进对不同来源的句子的解释。还对话语感知神经生成模型进行了研究，作为提高生成文本连贯性的扩展。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。