Mitigating Domain Mismatch in Machine Translation via Paraphrasing (2023)

Abstract: Quality of machine translation (MT) deteriorates significantly when translating texts having characteristics that differ from the training data, such as content domain. Although previous studies have focused on adapting MT models on a bilingual parallel corpus in the target domain, this approach is not applicable when no parallel data are available for the target domain or when utilizing black-box MT systems. To mitigate problems caused by such domain mismatch without relying on any corpus in the target domain, this study proposes a method to search for better translations by paraphrasing input texts of MT. To obtain better translations even for input texts from unforeknown domains, we generate their multiple paraphrases, translate each, and rerank the resulting translations to select the most likely one. Experimental results on Japanese-to-English translation reveal that the proposed method improves translation quality in terms of BLEU score for input texts from specific domains.

## introduction despite recent advances in machine translation (mt), translation quality still depends on the characteristics of the data on which the mt system is trained. therefore, for input texts having significantly different characteristics from the training data, there is a risk that translation quality may be degraded @xcite . to alleviate mismatches in one of such characteristics, content domain (henceforth, domain), an approach of transfer learning @xcite is commonly used, where a pre-trained mt model is fine-tuned on a parallel corpus in the target domain. however, there are many challenges in supporting a variety of domains. first of all, there are only a limited number of domains that have access to a bilingual parallel corpus sufficient for fine-tuning pre-trained mt models. even if parallel corpora are available for a large number of domains, then the time required for fine-tuning for each domain and the management cost for resulting models will not be negligible. more importantly, existing domain adaptation methods are not applicable in situations where we target a black-box mt system, such as google translate and deepl, even if they are already superior to pre-trained mt models in the domain of interest. this paper proposes a method to bridge the domain gap between sentences to be translated and mt training data without a need for additional training for a specific target domain as in existing domain adaptation methods, such as fine-tuning pre- ## related work paraphrasing input sentences has improved the performance of various natural language processing tasks such as document summarization @xcite and information extraction @xcite . paraphrasing has been studied also for mt. @xcite investigated manual pre-editing, including paraphrasing, to push the limit of existing mt services, and identified diverse types of pre-editing that can improve translation quality. however, there are two issues in automating paraphrasing for mt. one is that we lack a method for producing diverse (and accurate) paraphrases. past work on automatic paraphrasing for mt @xcite @xcite has examined only a limited variation, i.e., lexical and/or syntactic simplification, and observed quality improvement only in limited settings. another issue is that the effect of each particular paraphrasing is unpredictable due to the sensitivity of neural mt to input sentences @xcite . we thus need to assess the quality of mt outputs in a post-hoc manner rather than the quality of paraphrased sentences before translating them with the target mt system. ## proposed method to automatically bridge the gap between the domains of input sentences and mt training data, we propose a framework consisting of three steps shown in figure 1 . given an input sentence, we generate multiple paraphrases for it, translate each of the input sentence and multiple paraphrases using the target mt system, and select one candidate translation through reranking. in this section, we describe the first paraphrasing step and the third reranking step. for the second step, i.e., mt, we primarily assume a black-box system, such as online mt services. however, to explore better reranking, we also consider a glass-box setting, assuming that some information can be drawn from the target mt system. ## experiments we evaluated the effectiveness of the proposed method on three japanese-to-english translation tasks. for the sake of fair comparison, in our work, we built an mt system by ourselves instead of using a truly black-box one. ## conclusion in this study, to mitigate the domain mismatch between the domains of the input sentence and the training data of the target mt system, we proposed a framework that combines paraphrase generation and reranking. in particular, the combination of word-level paraphrase generation and glass-box reranking consistently improved translation quality in the two specific domains most significantly. our future work will focus on improving reranking and filtering word-level paraphrases to further improve performance.