RI: Small: ConnotationNet: Modeling Non-Literal Meaning in Context

RI：小：ConnotationNet：在上下文中建模非字面意义

• 批准号: 1714566
• 负责人: Yejin Choi
• 金额: $ 49.98万
• 依托单位: University of Washington
• 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: 2017-09-15 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1714566&HistoricalAwards=false
• 关键词: RI; Small; ConnotationNet; Modeling; Non

The major goal of this research is to develop a new computational framework to recover and reason about a wide range of connotative meanings in language, i.e., why something is written and how it will affect the readers. This contrasts with the vast majority of previous research on semantic processing, where the primary focus has been on understanding the denotational meaning of language, i.e., what is written in text. This research will create new computational solutions to a wide range of tasks that require understanding non-literal meaning in text, including societally important challenges such as automatic detection and revision of biases in modern literature and media that can work against minorities and underrepresented groups.This research will develop Connotation Frames as a new representation formalism to organize a variety of connotative implications associated with a particular choice of a predicate. This representation will substantially extend the existing resources of frame semantics, which has focused primarily on denotational meanings, by introducing new typed relations to encode various aspects of connotative meanings. Capitalizing on recent advances in distributional representation of words and phrases, this research will develop algorithms that can infer connotation frames from a large-scale natural language corpus, which reflects how connotative meanings arise from how people use language in context. The learned representations will be organized as ConnotationNet, an evolving broad-coverage connotation lexicon for words, frames, and phrases. Knowledge encoded in this lexicon will then be used for document-level text understanding, where partially present information in text will be combined with the rich connotative knowledge stored in ConnotationNet to infer the complete the document-level connotation of given text. In parallel, this research will seek new language generation models that can learn to revise or compose text with the desired connotative effects with specific focus on unwanted biases in modern literature and media against underrepresented groups.

这项研究的主要目的是开发一个新的计算框架，以恢复和推理语言中广泛的含义含义，即为什么要写东西以及如何影响读者。这与以前关于语义处理的绝大多数研究形成鲜明对比，在该研究中，主要重点一直在理解语言的含义，即文本中所写的内容。这项研究将为需要理解文本中的非文字意义的各种任务创建新的计算解决方案，包括社会上重要的挑战，例如自动检测和修改现代文学和媒体的偏见和媒体的修改，这些挑战和媒体可以与少数群体和不足的群体作用。这项研究将发展为与各种涉及的新表现形式相关联，这将发展为一种与特定选择相关的形式。该表示形式将通过引入新的键入关系来编码含义含义的各个方面，从而实质上扩展了框架语义的现有资源，该语义的现有资源主要集中在表示含义上。利用了单词和短语分布表示的最新进展，这项研究将开发算法，这些算法可以从大规模的自然语法语料库中推断出内涵框架，这反映了人们如何在上下文中使用语言。学习的表示形式将被组织为ConnotationNet，这是一种不断发展的单词，框架和短语的宽覆盖词。然后，该词典中编码的知识将用于文档级文本理解，其中部分呈现文本中的信息将与存储在ConnotationNet中的丰富内涵知识相结合，以推断给定文本的完整文档级别的含义。同时，这项研究将寻求新的语言生成模型，可以学会修改或撰写具有所需的内涵效果的文本，并特别关注现代文学和媒体对代表性不足的群体的不必要偏见。

项目成果

COMET: Commonsense Transformers for Knowledge Graph Construction

COMET：用于知识图构建的常识变压器

• 发表时间: 2019
• 期刊: Association for Computational Linguistics (ACL
• 作者: Bosselut, Antoine;Rashkin, Hannah;Sap, Maarten;Malaviya, Chaitanya;Celikyilmaz, Asli;Choi, Yejin
• 通讯作者: Choi, Yejin

The Risk of Racial Bias in Hate Speech Detection

• DOI: 10.18653/v1/p19-1163
• 发表时间: 2019-07
• 作者: Maarten Sap;Dallas Card;Saadia Gabriel;Yejin Choi;Noah A. Smith

Social Chemistry 101: Learning to Reason about Social and Moral Norms

• DOI: 10.18653/v1/2020.emnlp-main.48
• 发表时间: 2020-11
• 作者: Maxwell Forbes;Jena D. Hwang;Vered Shwartz;Maarten Sap;Yejin Choi

Adversarial Filters of Dataset Biases

• 发表时间: 2020-02
• 作者: Ronan Le Bras;Swabha Swayamdipta;Chandra Bhagavatula;Rowan Zellers;Matthew E. Peters;Ashish Sabharwal;Yejin Choi

On-the-Fly Attention Modulation for Neural Generation

• DOI: 10.18653/v1/2021.findings-acl.107
• 发表时间: 2021-01
• 作者: Yue Dong;Chandra Bhagavatula;Ximing Lu;Jena D. Hwang;Antoine Bosselut;J. Cheung;Yejin Choi