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

• 批准号: 1815455
• 负责人: Claire Cardie
• 金额: $ 29.08万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-01 至 2023-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1815455&HistoricalAwards=false
• 关键词: RI; Small; Collaborative; Research; Computational

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、评估和提出论点都是决策和推理过程中的关键要素。毫不奇怪，在日常生活中，在工作和家庭中，在我们的社会生活和公民生活中，我们都会遇到和构建大量的争论。尽管它们在我们的生活中无处不在，但大多数人并不擅长解释或产生论点。在最好的情况下，理解关于一个感兴趣的话题的大量争论性在线文本仍然是一项艰巨的任务。虽然存在许多用于表示、建模和可视化论点和争论性讨论的工具，但它们受到输入、组织和注释供工具使用的论点所需的大量人力的限制。因此，存在着迫切的需要，这个项目的目的是开发，从自然语言处理领域的自动化技术，以支持论证的各个方面。该项目将产生广泛的影响，包括为其他研究人员提供注释数据集和工具，用于分析和生成论点，通过研究生和本科生指导加强教育，并通过初中和高中女生的计划促进STEM教育的多样性。 该项目旨在在新兴的论点挖掘领域开辟新天地。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-可以组合和重用的方法，以支持一系列论证应用。该项目的重点是相互关联的研究线索，涵盖了三个关键领域的探索计算论证：（1）论点提取-使辩论文本的意义。借鉴结构化学习的最新发展，技术开发，以确定组件和结构的一个参数内的一个单一的文件或一个单一的在线对话。(2)论点聚合---根据讨论主题的各个方面，对从多个文档中提取的论点文本（例如句子，回合）的组成部分进行聚类。提出表征学习方法以更好地捕获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为中间表示的神经论元生成框架，以提高对不同来源句子的解释。一个话语感知的神经生成模型也被调查作为一个扩展，以提高所生成的文本的连贯性。这个奖项反映了NSF的法定使命，并已被认为是值得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的支持。

项目成果

Leveraging Topic Relatedness for Argument Persuasion

利用主题相关性进行论证

• DOI: 10.18653/v1/2021.findings-acl.386
• 发表时间: 2021
• 期刊: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021
• 作者: Zhao, Xinran;Durmus, Esin;Zhang, Hongming;Cardie, Claire
• 通讯作者: Cardie, Claire

The Role of Pragmatic and Discourse Context in Determining Argument Impact

语用和话语语境在确定论证影响力中的作用

• DOI: 10.18653/v1/d19-1568
• 发表时间: 2019
• 期刊: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP
• 作者: Durmus, Esin;Ladhak, Faisal;Cardie, Claire
• 通讯作者: Cardie, Claire

A Corpus for Modeling User and Language Effects in Argumentation on Online Debating

在线辩论辩论中对用户和语言效果进行建模的语料库

• 发表时间: 2019
• 期刊: Proceedings of the 57th Conference of the Association for Computational Linguistics (ACL
• 作者: Durmus, Esin;Cardie, Claire
• 通讯作者: Cardie, Claire

Modeling the Factors of User Success in Online Debate

• DOI: 10.1145/3308558.3313676
• 发表时间: 2019-05
• 期刊: The World Wide Web Conference
• 作者: Esin Durmus;Claire Cardie

Determining Relative Argument Specificity and Stance for Complex Argumentative Structures

• DOI: 10.18653/v1/p19-1456
• 发表时间: 2019-06
• 作者: Esin Durmus;Faisal Ladhak;Claire Cardie