Don’t Go Far Off: An Empirical Study on Neural Poetry Translation
Tuhin Chakrabarty, Arkadiy Saakyan, Smaranda Muresan · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · 2021
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 zeroshot setup; 2) bilingual vs. multilingual learning; and 3) language-family-specific models vs. mixed-language-family 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, COMET) and human evaluation metrics such as faithfulness (meaning and poetic style).Moreover, multilingual fine-tuning on poetic data outperforms bilingual fine-tuning on poetic data.