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arXiv 2403.14072cs.CL
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NLP中歧义类型的分类体系

A Taxonomy of Ambiguity Types for NLP

Paul G. Allen School of Computer Science & Engineering, University of Washington(华盛顿大学保罗·G·艾伦计算机科学与工程学院);Massachusetts Institute of Technology(麻省理工学院);Allen Institute for AI(艾伦人工智能研究所)

Margaret Y. Li, Alisa Liu, Zhaofeng Wu, Noah A. Smith

AI总结:

本文提出了一种面向英语的NLP歧义类型分类体系,旨在解决NLP系统对人类语言歧义处理不足的问题,通过对歧义数据进行有意义的划分,实现对数据集和模型性能的更细粒度评估。

摘要:

歧义是语言的重要组成部分,有助于说话者之间更有效的交流,但在NLP中常被忽视。近期研究表明,NLP系统可能难以掌握人类语言理解的某些要素,因为它们可能无法像人类在交流中那样自然地处理歧义。此外,不同类型的歧义可能服务于不同目的,并需要不同的解决方法,我们旨在调查语言模型的能力如何因类型而异。我们提出了一种英语中歧义类型的分类体系,以促进NLP分析。我们的分类体系有助于对语言歧义数据进行有意义的划分,从而实现对数据集和模型性能的更细粒度评估。

英文摘要:

Ambiguity is an critical component of language that allows for more effective communication between speakers, but is often ignored in NLP. Recent work suggests that NLP systems may struggle to grasp certain elements of human language understanding because they may not handle ambiguities at the level that humans naturally do in communication. Additionally, different types of ambiguity may serve different purposes and require different approaches for resolution, and we aim to investigate how language models' abilities vary across types. We propose a taxonomy of ambiguity types as seen in English to facilitate NLP analysis. Our taxonomy can help make meaningful splits in language ambiguity data, allowing for more fine-grained assessments of both datasets and model performance.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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