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

在自然语言处理中，共指解析涉及将文本中指向同一实体的所有单词和短语聚类在一起。例如，在“波洛先生向黑斯廷斯保证，他应该对他有信心”这句话中，“波洛先生”和“他”这两个字串指的是同一个人，而“黑斯廷斯”和“他”指的是不同的人物。解决这些引用是具有挑战性的，因为它需要语法、语义和世界知识的应用，而且它很重要，因为共同引用对于智能地理解文本的含义、回答问题、翻译、语料库洞察和许多其他应用程序至关重要。不幸的是，由于缺乏来自不同领域和世界语言的人工注释训练数据，目前的共同参考模型受到阻碍，主要是因为大规模收集此类数据既昂贵又耗时。这项CCRI计划拨款将通过为社区创建两个新资源，迈出打破共同参考数据瓶颈的第一步：(1)一个软件平台，为需要在文档中标记文本范围的任务提供廉价和准确的众包收集，以及(2)使用该平台收集的多域众包共同参考数据集。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，不像以前的数据集主要关注新闻专线文本，这将允许非标准领域的研究人员将共同参考系统集成到他们的建模管道中。该计划拨款还将支持有关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的完整提案，该提案旨在使用该平台创建一个更大的多语言共同参考数据集，并探索非共同参考数据标记任务，如问答。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.