DAGS: A Dependency-Based Dual-Attention and Global Semantic Improvement Framework for Metaphor Recognition

AbstractCurrent metaphor recognition mainly rely on Metaphor Detection Theory (MDT), such as the Metaphor Identification Procedure, which recognizes metaphors by comparing the basic meaning of target word with context meaning. Existing studies have gradually adopted literal annotations to model basic meanings, rejecting the aggregated meanings of target words. However, these methods ignore the problem of interference caused by literal annotations, and do not make full use of semantic expression relations of MDT, making the models difficult to detect and generalize. To address these challenges, we propose a dependency-based Dual-Attention and Global Semantic Improvement (DAGS) framework. DAGS first extracts literal annotations of target words as basic meaning from several mainstream corpora. Then, we apply dependency tree and dual-attention while filtering on input sentences and basic meanings. Finally, we improve the MDT to further consider the global semantic relationship on contexts. The DAGS can not only extract features from multiple information sources but alsoeffectively removes redundancy, while focusing on mission-critical information. We achieve state-of-the-art on several mainstream metaphor datasets (e.g., VUA ALL, VUAverb, TroFi and PSUCMC), which suggests that filtering and global semantic improvement of contexts is crucial for enhancing metaphor recognition performance.