III: Small: Mirador: Explainable Computational Models for Recognizing and Understanding Controversial Topics Encountered Online

III：小：Mirador：用于识别和理解网上遇到的有争议话题的可解释计算模型

• 批准号: 1813662
• 负责人: James Allan
• 金额: $ 49.97万
• 依托单位: University of Massachusetts Amherst
• 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: 2018-09-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1813662&HistoricalAwards=false
• 关键词: III; Small; Mirador; Explainable; Computational

This project aims to develop algorithms and tools that allow a person to recognize that a web page or other document discusses one or more topics that are controversial -- that is, about which there is strong disagreement within some sizeable group of people. The project will develop algorithms and tools that explain the controversy surrounding the topic, identifying the populations that disagree, the stances that they take, and how those stances conflict with each other. The advances in these algorithms will broaden the research community's understanding of how discussions and disagreements on topics can be modeled computationally and how that resulting information can be conveyed to a general user. The project will assist people in critical evaluation of on-line material and help them understand why a page is educative or why it is not. The aim of this project is to provide users with tools that illuminate the broader context of the topic or topics of a single page or document that someone finds. Previous work has shown that it is possible to recognize with reasonable accuracy that a document is part of a controversial topic, but that work is fragmented across different genres, demands more robust modeling and more thorough evaluation, and lacks explanatory power that can help a reader understand why and how a text is contentious. In this project, the researchers explore fundamental questions about how controversy can be modeled computationally so that it can be recognized "in the wild". The project also explores model variations that allow an algorithm to extract an explanation of the nature of the controversy. The project applies and extends text analysis and comparison techniques. It leverages powerful statistical language modeling methods as well as recent neural network (deep learning) approaches to represent text, its controversial nature, its stances, and their relationships, all extracted from Web pages and other documents. The modeling will be initially used offline to identify collections of topics known to be controversial and then adapt that collection by monitoring slowly-changing news sources and blog postings as well as ephemeral microblog sources of data to capture rapid changes in controversy. The researchers will make the resulting techniques available by providing an open-source example server.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.

该项目旨在开发算法和工具，使人们能够识别网页或其他文档讨论了一个或多个有争议的主题，即在相当大的群体中存在强烈分歧的主题。该项目将开发算法和工具来解释围绕该主题的争议，识别不同的人群、他们采取的立场以及这些立场如何相互冲突。这些算法的进步将扩大研究界对如何对主题的讨论和分歧进行计算建模以及如何将结果信息传达给一般用户的理解。该项目将帮助人们对在线材料进行批判性评估，并帮助他们理解为什么一个页面具有教育意义或为什么没有教育意义。该项目的目的是为用户提供工具，以阐明某人找到的单个页面或文档的主题或主题的更广泛背景。先前的工作表明，可以以合理的准确性识别文档是有争议主题的一部分，但该工作分散在不同的流派中，需要更强大的建模和更彻底的评估，并且缺乏可以帮助读者的解释力了解文本为何以及如何引起争议。在这个项目中，研究人员探索了如何对争议进行计算建模以使其能够“在野外”被识别的基本问题。该项目还探索了模型的变化，允许算法提取对争议性质的解释。该项目应用并扩展了文本分析和比较技术。它利用强大的统计语言建模方法以及最新的神经网络（深度学习）方法来表示文本、其有争议的性质、立场及其关系，所有这些都从网页和其他文档中提取。该模型最初将离线使用，以识别已知有争议的主题集合，然后通过监控缓慢变化的新闻来源和博客帖子以及短暂的微博数据源来调整该集合，以捕获争议中的快速变化。研究人员将通过提供开源示例服务器来提供由此产生的技术。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。

项目成果

Utility of Missing Concepts in Query-biased Summarization

缺失概念在查询偏向摘要中的效用

• DOI: 10.1145/3404835.3463121
• 发表时间: 2021-07
• 期刊: Proceedings of The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 21
• 作者: Sarwar, Sheikh Muhammad;Moraes, Felipe;Jiang, Jiepu;Allan, James
• 通讯作者: Allan, James

Unsupervised Explainable Controversy Detection from Online News

来自在线新闻的无监督可解释争议检测

• DOI: 10.1007/978-3-030-15712-8_60
• 发表时间: 2019-04
• 期刊: Proceedings of the European Conference on Information Retrieval
• 作者: Kim; Y. and
• 通讯作者: Y. and

Explaining Text Matching on Neural Natural Language Inference

解释神经自然语言推理中的文本匹配

• DOI: 10.1145/3418052
• 发表时间: 2020-09-16
• 期刊: ACM Transactions on Information Systems (TOIS)
• 作者: Youngwoo Kim;Myungha Jang;J. Allan
• 通讯作者: J. Allan

Equi-explanation Maps: Concise and Informative Global Summary Explanations

等解释图：简洁、信息丰富的全局概要解释

• DOI: 10.1145/3531146.3533112
• 发表时间: 2022-06-20
• 期刊: Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency
• 作者: Tanya Chowdhury;Razieh Rahimi;J. Allan
• 通讯作者: J. Allan

Query-driven Segment Selection for Ranking Long Documents

用于对长文档进行排名的查询驱动的段选择

• DOI: 10.1145/3459637.3482101
• 发表时间: 2021-09-10
• 期刊: Proceedings of the 30th ACM International Conference on Information & Knowledge Management
• 作者: Youngwoo Kim;Razieh Rahimi;Hamed Bonab;James Allan
• 通讯作者: James Allan