Towards Zero-Shot Multilingual Poetry Translation (2023)

Abstract: The application of machine translation in the field of poetry has always presented significant challenges. Conventional machine translation techniques are inadequate for capturing and translating the unique style of poetry. The absence of a parallel poetry corpus and the distinctive structure of poetry further restrict the effectiveness of traditional methods. This paper introduces a zero-shot method that is capable of translating poetry style without the need for a large-scale training corpus. Specifically, we treat poetry translation as a standard machine translation problem and subsequently inject the poetry style upon completion of the translation process. Our injection model only requires back-translation and easily obtainable monolingual data, making it a low-cost solution. We conducted experiments on three translation directions and presented automatic and human evaluations, demonstrating that our proposed method outperforms existing online systems and other competitive baselines. These results validate the feasibility and potential of our proposed approach and provide new prospects for poetry translation.

## introduction the process of translating poetry presents an intricate challenge within the field of machine translation. the prevalence of the neural machine translation (nmt) paradigm @xcite , which necessitates copious amounts of data to effectively train a model capable of producing accurate translations @xcite . unfortunately, the availability of parallel corpora that can be leveraged towards the training of a robust poetry translation system is currently inadequate. on the other hand, poetry is a manifestation of the unique imagination and creativity of the poet, as well as their distinctive writing style. as @xcite has pointed out, although nmt systems may succeed in translating the essence meaning of the poetry, the translation process inevitably disregards the writing style. as a result, two distinct research paths have emerged within the field of poetry translation. in an effort to preserve the poet's distinctive writing style, @xcite initially employed statistical machine translation, with a focus on maintaining the rhythm of the original work. this approach was subsequently refined by @xcite , who introduced the additional constraints of rhythm and rhyme into neural poetry translation. this yielded an acceptability rate of 78.2% in terms of translation quality. additionally, @xcite explored the use of unique tokens to govern the structure of the translated poetry. meanwhile, from the perspective of overcoming the low-resource challenge, researchers have been proved to be an effective method by utilizing out-of-domain data, such as song lyrics @xcite . however, the existence of gaps in genre and writing style between poetry and other forms of text, improvements made to current poetry do not necessarily result in significant advancements. this is because these methods remain limited to enhancing the representation of general domain text, rather than enabling the model to comprehend the intricate patterns present in poetry. encouragingly, as @xcite has provided poetic parallel corpora and demonstrated that fine-tuning with such data can greatly enhance the model's ability to adapt in poetry translation. regrettably, the collection of parallel text and the construction of corpora are prohibitively expensive for research purposes. to address the aforementioned concerns in a cost-effective manner, we proposed a novel zero-shot method for poetry translation that consists of two stages. during the first stage, the model concentrates on learning to translate general domain text, thereby guaranteeing the preservation of the meaning of the source sentence and the fluency of the generated translation. in the second stage, the model acquires the ability to inject poetic style into the sentences generated in the first stage, resembling the process of style transfer @xcite @xcite . to achieve this goal, we collected over 2 million poetry texts in the target languages, including english, portuguese, and chinese. we utilized a back-translation approach to separate the poetic style, resulting in a pseudo-parallel corpus that goes from general text to poetry text. our proposed method outperforms several competitive baselines, as evidenced by both automatic evaluation metrics and human evaluation results. the contributions of our work are as follows: ## methodology to overcome the current absence of parallel poetry corpus, a zero-shot poetry translation method was proposed. as displayed in figure 1 , the proposed approach comprises of two distinct stages: ordinary translation and style injection. ## experimental settings we conduct experiments on english, portuguese, and chinese poetry translation task to verify the effectiveness of the proposed method. ## main results ## analysis as shown in ## conclusions and future work this paper introduces a zero-shot poetry translation method, which reduces the difficulty of the whole poetry translation by splitting the process of translating the meaning of the poem and translating the poetic meaning in poetry translation. the proposed method also avoids the disadvantage of lack of parallel poetry corpus and reduces the cost of training. the experimental results, derived from both automatic and human evaluation, provide evidence of the efficacy of our proposed method. in the poetry translation, the model achieved superior outcomes in comparison to contemporary online systems. moreover, the proposed technique was evaluated by humans and found to inject poetic meaning into the translated poems, thereby bringing them closer to the standards of human translation and aligning more closely with human expectations for poetry. moving forward, our future work will focus on exploring the potential of our proposed method on more languages.