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，评估和生成论点都是决策和推理过程中的关键要素。毫不奇怪，随着在工作，在家中，在我们的社交生活和我们的公民生活中做出决定，每天都会遇到和构建许多论点。尽管他们在我们的生活中无处不在，但大多数人并不是在解释或产生论据方面特别熟练。充其量，理解有关感兴趣主题的大量论证在线文本仍然是一项艰巨的任务。尽管存在许多用于代表，建模和可视化论点和论证讨论的工具，但它们受到输入，组织和注释论证所需的大量人类努力的限制，以供工具使用。因此，存在着迫切的需求，该项目旨在从自然语言处理领域开发自动化技术，以支持论证的所有方面。该项目将产生广泛的影响，包括为其他研究人员提供带注释的数据集和工具，以分析和生成论据，通过研究生和本科指导来增强教育，并通过针对中学和高中女生的计划促进STEM教育多样性。 该项目旨在打破新兴的论证挖掘领域的新地面。它开发了一个计算模型的集合，这些模型构成了论证工具包的基础---可以组合和重新使用以支持一系列论证应用程序的方法。该项目的重点是研究的相关线索，其中涵盖了计算论证的三个关键探索领域：（1）参数提取---使论证文本有意义。利用结构化学习的最新发展，开发了技术来确定单个文档或单一转向中的参数的组件和结构。 （2）参数汇总---按照他们所解决的主题的方面从多个文档中绘制的论证文本的组成部分（例如句子，转弯）。提出了表示学习方法，以更好地捕获主题内容和论证方式。 （3）参数生成---通过重写构建连贯的论点。创建了具有关键短语提取作为中间表示的神经论证生成框架，以改善对不同来源的句子的解释。还研究了一种语言感知的神经产生模型，以提高生成文本的连贯性。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响审查标准，认为值得通过评估来获得支持。

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

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

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