Figurative language is ubiquitous in English. Yet, the vast majority of NLP
research focuses on literal language. Existing text representations by design
rely on compositionality, while figurative language is often non-compositional.
In this paper, we study the interpretation of two non-compositional figurative
languages (idioms and similes). We collected datasets of fictional narratives
containing a figurative expression along with crowd-sourced plausible and
implausible continuations relying on the correct interpretation of the
expression. We then trained models to choose or generate the plausible
continuation. Our experiments show that models based solely on pre-trained
language models perform substantially worse than humans on these tasks. We
additionally propose knowledge-enhanced models, adopting human strategies for
interpreting figurative language types : inferring meaning from the context and
relying on the constituent words' literal meanings. The knowledge-enhanced
models improve the performance on both the discriminative and generative tasks,
further bridging the gap from human performance.

本文研究了两种非组合式比喻语言 (成语和比喻) 的解释，并提出了基于人类策略的知识增强模型，从而改善了区分和生成任务的表现，进一步缩小了与人类表现的差距。