CRII: RI: Learning Structured Prediction Models with Auxiliary Supervision

CRII：RI：学习具有辅助监督的结构化预测模型

• 批准号: 1760523
• 负责人: Kai-Wei Chang
• 金额: $ 17.09万
• 依托单位: University of California-Los Angeles
• 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: 2017-07-01 至 2020-02-29
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1760523&HistoricalAwards=false
• 关键词: CRII; RI; Learning; Structured; Prediction; CRII; RI; Learning; Structured; Prediction

Many machine learning problems involve making joint predictions over a set or a sequence of mutually dependent outputs. As an example, consider recognizing a handwritten word, where each character must be recognized in order for the word to be understood. It is important to consider the correlations between the predictions of adjacent characters to aid the individual predictions of characters. Structured prediction models are proposed to solve problems of this type. They have been shown to be successful in many real-world applications, including speech recognition, natural language understanding, and object detection in images. Despite its success, training a structured prediction model requires an extensive collection of training data. However, obtaining human annotations with complex structures is costly. For example, it takes a professionally trained linguist several minutes to label a syntactic parse tree for a single sentence, making it expensive to obtain high-quality annotations. The objective of this research is to develop methods that utilize learning signals that do not directly aim to achieve the target tasks. The outcome of this project will create a fundamental shift in the applicability of structured prediction models, enabling applications in which complex decisions are required and annotated data are expensive to acquire. This will bring new collaboration opportunities with other areas, including education, healthcare, and social and behavioral sciences.The goals of this project are to study algorithms for training structured prediction models from heterogeneous learning signals, design automatic algorithms for mining useful information from massive columns of structured and unstructured data, and apply the proposed techniques in real-world applications. The proposed algorithms will be evaluated on a broad range of natural language processing applications, including the algebra word problem, co-reference resolution, and grammatical error correction. The results of the project will be disseminated by publishing papers, releasing open-source software and data sets, organizing workshops and tutorials, and creating new courses on natural language processing and machine learning.

许多机器学习问题涉及对集合或一系列相互依赖的输出进行联合预测。作为一个例子，请考虑识别一个手写的单词，必须识别每个字符才能理解单词。重要的是要考虑相邻字符的预测之间的相关性，以帮助字符的个体预测。提出了结构化预测模型来解决此类问题。它们已被证明在许多现实世界中都成功，包括语音识别，自然语言理解和图像中的对象检测。尽管取得了成功，但培训结构化的预测模型仍需要大量的培训数据收集。但是，获得具有复杂结构的人类注释是昂贵的。例如，它需要一个经过专业训练的语言学家几分钟才能用一句话标记一句话的句法解析树，从而获得高质量的注释变得昂贵。这项研究的目的是开发利用不直接旨在实现目标任务的学习信号的方法。该项目的结果将在结构化预测模型的适用性上产生根本的转变，从而使需要进行复杂决策的应用程序，并获得带注释的数据昂贵。这将为其他领域带来新的合作机会，包括教育，医疗保健以及社会和行为科学。该项目的目标是研究算法，用于培训从异构学习信号，设计自动算法的结构化预测模型，以挖掘有用的有用信息，从结构化和非结构数据的大量列中，并应用了建议的技术，并应用了现实的技术应用程序。提出的算法将在广泛的自然语言处理应用中进行评估，包括代数单词问题，共同参考分辨率和语法误差校正。该项目的结果将通过发布论文，释放开源软件和数据集，组织研讨会和教程以及创建有关自然语言处理和机器学习的新课程来传播。