用语言模型验证习语可分解性假说，发现其与句法灵活性关系弱。
Rethinking the Idiomaticity Decomposability Hypothesis: Evidence from Distributional Learning
- 用上下文语言模型模拟习语学习过程，内部分析可分解性
- 模型推导的可分解性与人类判断相关性弱，与句法灵活度负相关
- 习语表征稳定受意外度、可分解性、频率共同影响，可分解性影响最显著
习语可分解性指其构成成分意义对整体隐喻义的贡献程度。传统观点认为可分解性预测句法灵活性，而基于使用的方法则将其归因于分布经验，如说话人熟悉度和可预测性。本研究利用上下文语言模型作为受控的分布学习者，提出一种模型内部的可分解性度量，并将其与人类评分、句法灵活性及可预测性关联，同时追踪预训练过程中习语的学习过程。结果显示，模型推导的可分解性与人类判断的相关性较弱，且与句法灵活性呈现微弱但一致的负相关。预训练分析表明，习语表征的稳定并非仅由频率决定；意外度、可分解性和频率均起作用，其中可分解性表现出最强的训练依赖效应。
原文摘要 · Abstract (English)
Idioms can be analysed in terms of their decomposability, the extent to which constituent meanings contribute to the figurative whole. Decomposability is thought to predict syntactic flexibility. Usage-based accounts instead attribute idiom behaviour to distributional experience, such as speaker familiarity and predictability. We examine these views using contextualised language models as controlled distributional learners. We propose a model-internal measure of decomposability and relate it to human ratings, syntactic flexibility, and predictability while tracking idiom learning during pretraining. Model-derived decomposability correlates weakly with human judgments and shows a small but consistent negative relationship with syntactic flexibility. Pretraining analyses show that stabilisation of idiom representations in models is not explained by frequency alone. Instead, surprisal, decomposability, and frequency all contribute, with decomposability showing the strongest training-dependent effect.
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