Can ChatGPT's Performance be Improved on Verb Metaphor Detection Tasks? Bootstrapping and Combining Tacit Knowledge

Anonymous

Abstract: Metaphors Detection, as an important task in the field of Natural Language Processing, has been receiving sustained academic attention in recent years. Current research focuses on the development of supervised Metaphors Detection systems, which usually require large-scale, high-quality labeled data support. With the rapid development of large-scale generative language models (e.g., ChatGPT), they have been widely used in a number of domains, including automatic summarization, sentiment analysis, and question and answer systems. However, it is worth noting that the use of ChatGPT for unsupervised Metaphors Detection task is often challenged with less-than-expected performance. Therefore, the aim of this paper is to explore how to bootstrap and combine ChatGPT by detecting the most prevalent verb metaphors among metaphors. Our approach first utilizes ChatGPT to obtain literal collocations of target verbs and subject-object pairs of verbs in the text to be detected. Subsequently, these literal collocations and subject-object pairs are mapped to the same set of topics, and finally the verb metaphors are detected through the analysis of entailment relations. The experimental results show that our method achieves the best performance on the unsupervised verb Metaphors Detection task compared to past unsupervised methods or direct prediction using ChatGPT.

Paper Type: long

Research Area: Efficient/Low-Resource Methods for NLP

Contribution Types: Approaches low compute settings-efficiency

Languages Studied: English