Large Language Models for Classical Chinese Poetry Translation: Benchmarking, Evaluating, and Improving

ACL ARR 2024 December Submission1344 Authors

Abstract: Different from the traditional translation tasks, classical Chinese poetry translation requires both adequacy and fluency in translating culturally and historically significant content and linguistic poetic elegance. Large language models (LLMs) with impressive multilingual capabilities may bring a ray of hope to achieve this extreme translation demand. This paper first introduces a suitable benchmark (PoetMT) where each Chinese poetry has a recognized elegant translation. Meanwhile, we propose a new metric based on GPT-4 to evaluate the extent to which current LLMs can meet these demands. Our empirical evaluation reveals that the existing LLMs fall short in the challenging task. Hence, we propose a Retrieval-Augmented Machine Translation (RAT) method which incorporates knowledge related to classical poetry for advancing the translation of Chinese poetry in LLMs. Experimental results show that RAT consistently outperforms all comparison methods regarding wildly used BLEU, COMET, BLEURT, our proposed metric, and human evaluation.

Paper Type: Long

Research Area: Machine Translation

Research Area Keywords: Machine Translation

Contribution Types: NLP engineering experiment, Approaches low compute settings-efficiency

Languages Studied: English, Chinese

Submission Number: 1344