What is the Best Way for ChatGPT to Translate Poetry?
Shanshan Wang, Derek F. Wong, Jingming Yao, Lidia S. Chao

TL;DR
This paper explores how ChatGPT can be optimized for translating poetry between English and Chinese, proposing a new explanation-guided method that improves translation quality as validated by human and AI evaluations.
Contribution
It introduces the EAPMT method that uses monolingual poetry explanations to enhance ChatGPT's translation of poetry, advancing literary machine translation techniques.
Findings
EAPMT outperforms traditional ChatGPT translation methods.
Human and AI evaluations confirm the effectiveness of EAPMT.
Refined evaluation criteria better capture poetry translation quality.
Abstract
Machine translation (MT) has historically faced significant challenges when applied to literary works, particularly in the domain of poetry translation. The advent of Large Language Models such as ChatGPT holds potential for innovation in this field. This study examines ChatGPT's capabilities in English-Chinese poetry translation tasks, utilizing targeted prompts and small sample scenarios to ascertain optimal performance. Despite promising outcomes, our analysis reveals persistent issues in the translations generated by ChatGPT that warrant attention. To address these shortcomings, we propose an Explanation-Assisted Poetry Machine Translation (EAPMT) method, which leverages monolingual poetry explanation as a guiding information for the translation process. Furthermore, we refine existing evaluation criteria to better suit the nuances of modern poetry translation. We engaged a panel of…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
Taxonomy
TopicsTopic Modeling · Artificial Intelligence in Healthcare and Education · Text Readability and Simplification
MethodsAttention Is All You Need · Softmax · Layer Normalization · Linear Layer · Byte Pair Encoding · Label Smoothing · Adam · Residual Connection · Position-Wise Feed-Forward Layer · Multi-Head Attention
