IEKG：一个面向习语表达的常识知识图谱
IEKG: A Commonsense Knowledge Graph for Idiomatic Expressions
AI总结：
本文构建面向习语比喻解释的常识知识图谱IEKG，扩展ATOMIC2020将PTLM转化为知识模型，实验证明其可提升习语理解并泛化至未见习语。
摘要：
习语表达（IE）的处理与理解一直困扰着预训练语言模型（PTLM），因为其含义具有非组合性。以往工作通过使用包含IE的句子对PTLM进行微调来实现IE理解，与之不同，本研究构建了IEKG，这是一个用于IE比喻性解释的常识知识图谱。它扩展了已建立的ATOMIC2020图谱，将PTLM转化为知识模型（KM），用于编码和推断与IE使用相关的常识知识。实验表明，多种PTLM都可以借助IEKG转化为KM。我们通过自动评估和人工评估验证了IEKG的质量以及训练后KM的能力。通过在自然语言理解中的应用，我们表明注入IEKG知识的PTLM表现出更强的IE理解能力，并且能够泛化到训练期间未见过的IE。
英文摘要：
Idiomatic expression (IE) processing and comprehension have challenged pre-trained language models (PTLMs) because their meanings are non-compositional. Unlike prior works that enable IE comprehension through fine-tuning PTLMs with sentences containing IEs, in this work, we construct IEKG, a commonsense knowledge graph for figurative interpretations of IEs. This extends the established ATOMIC2020 graph, converting PTLMs into knowledge models (KMs) that encode and infer commonsense knowledge related to IE use. Experiments show that various PTLMs can be converted into KMs with IEKG. We verify the quality of IEKG and the ability of the trained KMs with automatic and human evaluation. Through applications in natural language understanding, we show that a PTLM injected with knowledge from IEKG exhibits improved IE comprehension ability and can generalize to IEs unseen during training.
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。
