关键词: 隐喻, 隐喻识别, 预训练模型, 语言学规则, 外部资源

Abstract: Existing research work on metaphor detection task mostly adopted deep learning techniques and did not utilize linguistic rules in depth, which was mainly manifested in the defective representation of semantic and basic meanings of the target words involved in the rules, resulting in the model's inability to focus on the differences between the target words and more relevant contextual words, and the boundaries between the basic meanings and the contextual meanings were still fuzzy. To address the above problems, a metaphor detection model MeRL (Metaphor Detction for Improving Representation in Linguistic Rules) was proposed. First, it enhanced the semantic representation of target words involved in both Selectional Preference Violation (SPV), and Metaphor Identification Procedure (MIP) rules; second, it characterized the basic meaning of target words in Metaphor Identification Procedure rules; and finally, it fused the rule-based design of SPV and MIP modules to jointly identify metaphors. The experimental results on the benchmark datasets VUA-18, VUA Verb, and MOH-X showed that compared with MelBERT (Metaphor-aware late interaction over BERT) and other models, the F1 values of the proposed model were improved by 0.6, 0.9, and 1.2 percentage points respectively, and the detection of metaphors was more accurate. Also, the results of doing zero-shot transfer learning on the TroFi dataset showed that the F1 value was improved by 0.7 percentage points and the generalization ability of the model was stronger.

Key words: metaphor, metaphor detection, pre-training model, linguistic rule, external resource