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 professional poets
for assessments, complemented evaluations by using GPT-4. The results from both
human and machine evaluations demonstrate that our EAPMT method outperforms
traditional translation methods of ChatGPT and the existing online systems.
This paper validates the efficacy of our method and contributes a novel
perspective to machine-assisted literary translation.

机器翻译在文学作品中，特别是诗歌翻译领域一直面临重大挑战。本研究通过使用有针对性的提示和小样本情景来评估 ChatGPT 在英中诗歌翻译任务中的性能，并提出了一种基于解释的诗歌机器翻译 (EAPMT) 方法，该方法利用单语诗歌解释作为翻译过程的指导信息。通过专业诗人的评估和使用 GPT-4 进行评估，人机评估结果都显示出我们的 EAPMT 方法优于 ChatGPT 的传统翻译方法和现有的在线系统。本文验证了我们的方法的有效性，并为机器辅助的文学翻译提供了新的视角。

ChatGPT 如何翻译诗歌的最佳方式？

What is the Best Way for ChatGPT to Translate Poetry?

Despite constant improvements in machine translation quality, automatic
poetry translation remains a challenging problem due to the lack of
open-sourced parallel poetic corpora, and to the intrinsic complexities
involved in preserving the semantics, style, and figurative nature of poetry.
We present an empirical investigation for poetry translation along several
dimensions: 1) size and style of training data (poetic vs. non-poetic),
including a zero-shot setup; 2) bilingual vs. multilingual learning; and 3)
language-family-specific models vs. mixed-multilingual models. To accomplish
this, we contribute a parallel dataset of poetry translations for several
language pairs. Our results show that multilingual fine-tuning on poetic text
significantly outperforms multilingual fine-tuning on non-poetic text that is
35X larger in size, both in terms of automatic metrics (BLEU, BERTScore) and
human evaluation metrics such as faithfulness (meaning and poetic style).
Moreover, multilingual fine-tuning on poetic data outperforms \emph{bilingual}
fine-tuning on poetic data.

本文旨在探索机器翻译在诗歌翻译方面的应用，研究并比较了诗歌和非诗歌语料库大小、双语和多语言学习、以及使用语系特定模型和混合多语言模型的效果，得出多语言微调在诗歌翻译中具有重要性和优越性。