CCRI: Planning: Planning for the Development of a Platform to Support Multilingual and Multi-Domain Coreference Annotation for Natural Language Processing Research

CCRI：规划：规划开发支持自然语言处理研究多语言、多领域共指标注的平台

• 批准号: 1925548
• 负责人: Brendan O'Connor
• 金额: $ 10万
• 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: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1925548&HistoricalAwards=false
• 关键词: CCRI; Planning; Development; Platform; Support

In natural language processing, coreference resolution involves clustering together all words and phrases within a text that refer to the same entity. For example, in the sentence "Monsieur Poirot assured Hastings that he ought to have faith in him," the strings "Monsieur Poirot" and "him" refer to the same person, while "Hastings" and "he" refer to a different character. Resolving these references is challenging because it requires the application of syntactic, semantic, and world knowledge, and it is important since coreference is essential to intelligently understand the meaning of text for question answering, translation, corpus insights, and many other applications. Unfortunately, current coreference models are held back by the lack of human-annotated training data from various domains and world languages, mainly because it is expensive and time-consuming to collect such data at scale.This CCRI planning grant will take the first step toward breaking the coreference data bottleneck by creating two new resources for the community: (1) a software platform that facilitates cheap and accurate crowdsourced collection for tasks that require labeling text spans within documents, and (2) a multi-domain crowdsourced coreference dataset collected using this platform. The dataset resource will contain data from a variety of different domains (such as books and web forums), unlike prior datasets that focus primarily on newswire text, which will allow researchers who work on non-standard domains to integrate coreference systems into their modeling pipelines. This planning grant will also support discussions and conference workshops about the platform and data resources; the resulting community feedback will be incorporated into a CCRI full proposal that aims to use the platform to create a much larger and multilingual coreference dataset, as well as explore non-coreference data labeling tasks such as question answering.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.

在自然语言处理中，共指关系解析涉及将文本中引用同一实体的所有单词和短语聚在一起。例如，在“波罗先生向黑斯廷斯保证他应该信任他”这句话中，“波罗先生”和“他”指的是同一个人，而“黑斯廷斯”和“他”指的是不同的角色。解决这些引用是具有挑战性的，因为它需要应用句法、语义和世界知识，而且由于共指对于智能地理解用于问题回答、翻译、语料库洞察和许多其他应用的文本的意义是必不可少的。遗憾的是，由于缺乏来自不同领域和世界语言的人工标注的训练数据，当前的共引模型受到阻碍，主要是因为大规模收集此类数据既昂贵又耗时。该CCRI规划赠款将通过为社区创建两个新资源来朝着打破共引数据瓶颈迈出第一步：(1)一个软件平台，它促进廉价而准确的众包收集，用于需要在文档中标记文本跨度的任务；(2)使用该平台收集的多域众包共引数据集。数据集资源将包含来自各种不同领域(如书籍和网络论坛)的数据，这与以前主要关注新闻报道文本的数据集不同，这将允许研究非标准领域的研究人员将共同参考系统整合到他们的建模管道中。这笔规划拨款还将支持关于该平台和数据资源的讨论和会议研讨会；由此产生的社区反馈将被纳入CCRI的全面提案中，该提案旨在利用该平台创建一个更大的、多语言的共参考数据集，并探索问题回答等非共参考数据标签任务。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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

ezCoref: Towards Unifying Annotation Guidelines for Coreference Resolution

ezCoref：迈向统一共指解析注释指南

• 发表时间: 2023
• 期刊: Findings of the Association for Computational Linguistics: EACL 2023
• 作者: Gupta, Ankita;Karpinska, Marzena;Zhao, Wenlong;Krishna, Kalpesh;Merullo, Jack;Yeh, Luke;Iyyer, Mohit;O'Connor, Brendan
• 通讯作者: O'Connor, Brendan