Improvements and Extensions on Metaphor Detection

TL;DR

This paper introduces a Transformer-based model for metaphor detection that significantly outperforms previous models, extends the task to classify entire texts, and updates benchmark datasets with cleaner annotations.

Contribution

It presents a novel Transformer-based approach for metaphor detection, extends the task to broader NLU applications, and re-benchmarks datasets with improved annotations.

Findings

Transformer model improves F-1 score by up to 28.39%

Extended MD to classify metaphoricity of entire texts

Re-benchmarked datasets with cleaner annotations

Abstract

Metaphors are ubiquitous in human language. The metaphor detection task (MD) aims at detecting and interpreting metaphors from written language, which is crucial in natural language understanding (NLU) research. In this paper, we introduce a pre-trained Transformer-based model into MD. Our model outperforms the previous state-of-the-art models by large margins in our evaluations, with relative improvements on the F-1 score from 5.33% to 28.39%. Second, we extend MD to a classification task about the metaphoricity of an entire piece of text to make MD applicable in more general NLU scenes. Finally, we clean up the improper or outdated annotations in one of the MD benchmark datasets and re-benchmark it with our Transformer-based model. This approach could be applied to other existing MD datasets as well, since the metaphoricity annotations in these benchmark datasets may be outdated. Future research efforts are also necessary to build an up-to-date and well-annotated dataset consisting of longer and more complex texts.