RI:Medium:Collaborative Research:Developing a uniform meaning representation for natural language processing

RI：中：协作研究：为自然语言处理开发统一的含义表示

• 批准号: 1763926
• 负责人: Nianwen Xue
• 金额: $ 39.92万
• 依托单位: Brandeis University
• 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: 2018-08-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1763926&HistoricalAwards=false
• 关键词: RI; Medium; Collaborative; Research; Developing

The use of intelligent agents that can communicate with us in human language has become an essential part of our daily lives. Today's intelligent agents can respond appropriately to many things we say or text to them, but they cannot yet communicate fully like humans. They lack our general ability to arrive quickly at accurate and relevant interpretations of what others communicate to us and to form appropriate responses, particularly in sustained interactions. The typical way we teach a machine to acquire such ability is to provide it with approximations of the meanings of utterances in the contexts in which they have occurred in the past. Over the years these approximations have become increasingly rich and detailed, enabling ever more sophisticated systems for interacting with computers using natural language, such as searching for information, getting up-to-date recommendations for products and services, and translating foreign languages. The goal of this project is to bring together linguists and computer scientists to jointly develop a practical meaning representation formalism based on these rich approximations that can be applied to a much more diverse set of languages. This will allow us to use machine learning to develop techniques to automatically translate human utterances into our meaning formalism. In turn, this will enable intelligent agents to acquire more advanced communication capabilities, and for a wider range of languages. The languages considered for the project include those spoken by large populations such as English, Chinese and Arabic, as well as native tongues of smaller groups such as Norwegian, and Arapaho and Kukama-Kukamira, two indigenous languages of the Americas. As such, this project will help bring modern technology to smaller groups so that all people can benefit equally from technological advancement. The project will also contribute to the development of the US workforce by training a new generation of researchers on cutting-edge technologies in artificial intelligence. This project brings together an interdisciplinary team of linguists and computer scientists from three institutions to jointly develop a Uniform Meaning Representation (UMR). UMR is a practical, formal, computationally tractable, and cross-linguistically valid meaning representation of natural language that can impact a wide range of downstream applications requiring deep natural language understanding (NLU). UMR will extend existing meaning representations to include quantifier types and relations, modality, negation, tense and aspect, and be tested on a typologically diverse set of languages. Methods and techniques for UMR annotation, parsing and generation, and evaluation will be uniform across languages. The project will also develop novel algorithms and models for UMR-based broad-coverage and general-purpose multilingual semantic parsers. Students participating in the project will receive training in the full cycle of conceptualizing, producing, processing, and consuming meaning representations at the sites of participating institutions. This project will help to build a community of NLP researchers that will contribute to the development of UMR-based data and tools and advance the state of the art in Natural Language Processing (NLP) in particular, and Artificial Intelligence (AI) in general.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.

使用能够用人类语言与我们交流的智能代理已成为我们日常生活的重要组成部分。 今天的智能体可以对我们说的或发的短信做出适当的反应，但它们还不能像人类一样完全交流。它们缺乏我们的一般能力，无法对他人传达给我们的信息迅速做出准确和相关的解释，也无法形成适当的回应，特别是在持续的互动中。 我们教机器获得这种能力的典型方法是为它提供在过去发生的上下文中话语含义的近似值。 多年来，这些近似值变得越来越丰富和详细，使越来越复杂的系统能够使用自然语言与计算机进行交互，例如搜索信息，获得最新的产品和服务推荐以及翻译外语。 这个项目的目标是汇集语言学家和计算机科学家，共同开发一个实用的意义表示形式主义，基于这些丰富的近似，可以应用于更多样化的语言集。 这将使我们能够使用机器学习来开发技术，将人类的话语自动转换为我们的意义形式主义。反过来，这将使智能代理获得更先进的通信能力，并适用于更广泛的语言。 该项目考虑的语言包括大量人口使用的语言，如英语、汉语和阿拉伯语，以及较小群体的母语，如挪威语和美洲的两种土著语言阿拉帕霍语和库卡马-库卡米拉语。 因此，该项目将有助于将现代技术带给较小的群体，使所有人都能平等地从技术进步中受益。 该项目还将通过培训新一代人工智能尖端技术的研究人员，为美国劳动力的发展做出贡献。 该项目汇集了来自三个机构的语言学家和计算机科学家组成的跨学科团队，共同开发统一含义表示（UMR）。UMR是一种实用的、正式的、计算上易于处理的、跨语言有效的自然语言含义表示，可以影响需要深度自然语言理解（NLU）的广泛下游应用。 UMR将扩展现有的意义表示，包括量词类型和关系，模态，否定，时态和方面，并在类型学上不同的语言集进行测试。 UMR注释、解析和生成以及评估的方法和技术将在不同语言之间统一。 该项目还将为基于UMR的广泛覆盖和通用多语言语义解析器开发新的算法和模型。 参与该项目的学生将在参与机构的网站上接受概念化，生产，加工和消费意义表征的全周期培训。 该项目将帮助建立一个NLP研究人员社区，为基于UMR的数据和工具的开发做出贡献，并推动自然语言处理（NLP）和人工智能（AI）的发展。该奖项反映了NSF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。

项目成果

Abstract Meaning Representation for Gesture

手势的抽象意义表示

• 发表时间: 2022
• 期刊: Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
• 作者: Richard Brutti, Lucia Donatelli

Annotating Temporal Dependency Graphs via Crowdsourcing

通过众包注释时间依赖图

• DOI: 10.18653/v1/2020.emnlp-main.432
• 发表时间: 2020
• 期刊: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP
• 作者: Yao, Jiarui;Qiu, Haoling;Min, Bonan;Xue, Nianwen
• 通讯作者: Xue, Nianwen

Modeling Quantification and Scope in Abstract Meaning Representations

抽象意义表示中的量化和范围建模

• 发表时间: 2019
• 期刊: Proceedings of the First International Workshop on Designing Meaning Representations
• 作者: Pustejovksy, James;Xue, Nianwen;Lai, Kenneth
• 通讯作者: Lai, Kenneth

Modal Dependency Parsing via Language Model Priming

• DOI: 10.18653/v1/2022.naacl-main.211
• 发表时间: 2022
• 作者: Jiarui Yao;Nianwen Xue;Bonan Min

Parsing Meaning Representations: Is Easier Always Better?

• DOI: 10.18653/v1/w19-3304
• 发表时间: 2019
• 期刊: Proceedings of the First International Workshop on Designing Meaning Representations
• 作者: Zi Lin