BSF: 2016257: Building Models for Reading Comprehension in Specialized Domains from Scratch

BSF：2016257：从头开始构建专业领域的阅读理解模型

• 批准号: 1737230
• 负责人: Vivek Srikumar
• 金额: $ 3.5万
• 依托单位: University of Utah
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1737230&HistoricalAwards=false
• 关键词: BSF; 2016257; Building; Models; Reading

Machine learning algorithms are increasingly allowing people to search, structure, and access the textual information created daily in every possible domain. In areas with abundant annotated data, where supervised learning algorithms can be applied, algorithms for text understanding have had success in structuring text and providing natural language interfaces. When building a system in a new domain for which there is little to no data, however, data collection and annotation data can be prohibitively expensive. This project explores a protocol for developing text understanding systems that read text and provide a natural language interface in a particular domain (such as biology or history) -- this can allow specialized communities to have digital access to data that is otherwise locked in text. The project also trains students as part of an international collaboration -- this award supports travel of the US-based researchers to collaborate in a project funded by the US-Israel Binational Science Foundation. The project encompasses both data collection and model training, and considers the interaction between the two. To replace expert annotations it uses crowdsourcing workers in an iterative procedure that starts training a structured predictor from almost no data. It creates an interactive framework in which users ask questions and verify candidate answers that are later used to retrain the system. It aims to jointly train over multiple domains, and use domain adaptation methods to transfer knowledge from one domain to another. 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.

机器学习算法越来越多地允许人们搜索、构建和访问每天在每个可能的领域创建的文本信息。在具有丰富注释数据的领域，可以应用监督学习算法，文本理解算法在构建文本和提供自然语言接口方面取得了成功。然而，当在几乎没有数据的新领域中构建系统时，数据收集和注释数据可能非常昂贵。 该项目探索了一种用于开发文本理解系统的协议，该系统可以阅读文本并在特定领域（如生物学或历史）提供自然语言界面-这可以允许专业社区以数字方式访问否则被锁定在文本中的数据。 该项目还培训学生作为国际合作的一部分-该奖项支持美国研究人员在美国-以色列两国科学基金会资助的项目中进行合作。 该项目包括数据收集和模型训练，并考虑两者之间的相互作用。为了取代专家注释，它在一个迭代过程中使用众包工作人员，从几乎没有数据开始训练结构化预测器。它创建了一个交互式框架，用户可以在其中提出问题并验证候选答案，这些答案随后将用于重新训练系统。它旨在在多个领域进行联合训练，并使用领域适应方法将知识从一个领域转移到另一个领域。该奖项反映了NSF的法定使命，并被认为是值得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的支持。

项目成果

On the Limits of Learning to Actively Learn Semantic Representations

• DOI: 10.18653/v1/k19-1042
• 发表时间: 2019-10
• 期刊: ArXiv
• 作者: Omri Koshorek;Gabriel Stanovsky;Yichu Zhou;Vivek Srikumar;Jonathan Berant