Automatic Post-Editing for V ietnamese (2021)

Abstract: Automatic post-editing (APE) is an important remedy for reducing errors of raw translated texts that are produced by machine translation (MT) systems or software-aided translation. In this paper, we present a systematic approach to tackle the APE task for Vietnamese. Specifically, we construct the first large-scale dataset of 5M Vietnamese translated and corrected sentence pairs. We then apply strong neural MT models to handle the APE task, using our constructed dataset. Experimental results from both automatic and human evaluations show the effectiveness of the neural MT models in handling the Vietnamese APE task.

## introduction recent research has placed significant advancements for automatic machine translation @xcite @xcite . the high-quality mt output has been widely adopted by professional translators into their translation workflow to save time and reduce translation errors @xcite . translating chinese novels to vietnamese is an important task. in the last ten years, there are about 30k chinese novels describing fiction stories, that are available in vietnamese with ∼ 80k active readers and ∼ 600k novel chapter views daily from the three most popular vietnamese websites for reading novels. 123 but, translating the chinese novels to vietnamese is still challenging. the reason is that in fact, readers prefer reading the novels translated using the traditional language style rather than the modern language style used in news articles (e.g. using "tiểu nữ nhi" little girl instead of "cô bé" little girl ). note that current general-purpose mt systems (e.g., google translate), trained on modern language style-focused bilingual corpora, cannot satisfy the reader preference. the well-known workflow/guideline used for translating the chinese novels to vietnamese consists of three steps: foot_3 note that the final editing step is very timeconsuming due to the large amount of humanmanual work. thus automatic post-editing (ape) might be involved in this final step, helping to reduce the human effort in editing the translated text @xcite . to the best of our knowledge, there is no previous study on ape for vietnamese. in this paper, we formulate the ape problem for vietnamese as a monolingual translation task. we first construct a large-scale dataset consisting of translated and corrected sentence pairs. we then use our dataset to train a state-of-the-art neural mt model to automatically post-edit the translated sentences, and compare these models under various settings. our contributions are summarized as: ## our dataset this section presents our large-scale dataset for the vietnamese ape task. ## experimental setup this section presents neural mt models as well as their training details that we employ for evaluation. ## related work our work is the first one to automatically handle the task of correcting the vietnamese translated text of chinese novels. however, ape is not new and has proved to be an effective approach to handle the inaccuracies of raw mt output @xcite @xcite @xcite . ape approaches cover two main research directions including statistical mt-based models @xcite and neural mt-based models @xcite . in particular, @xcite propose a statistical phrase-based mt system to post-edit the output of a rule-based mt system by handling the typical errors made by the rule-based one. likewise, @xcite utilize statistical information from a pre-trained statistical mt model to post-edit the output of another statistical mt model. @xcite propose to use bidirectional lstm encoder-decoder for ape and found that it performs better than statistical phrasebased ape. correia and martins (2019 ) present an effective ape approach where they fine-tune pre-trained bert models @xcite on both the bert-based encoder and decoder. ## conclusion we have presented the first work of ape for vietnamese to automatically correct the vietnamese translated text of chinese novels. we construct the first large-scale dataset of 5m translated and corrected sentence-level pairs, extracted from 99.5k translated and corrected chapter-level pairs from 183 novels, for the vietnamese ape task. we then compare three mt models using our dataset under in-domain and out-of-domain data splitting schemes. experimental results from both the automatic and human evaluations show that the neural mt models help improve the quality of the translated text. specifically, "transformer-large" achieves the best performances w.r.t. the ter, gleu, bleu scores and human votes, helping to reduce the human effort in editing the translated novels, and serving as a strong model for future research and applications. we also publicly release our dataset and model checkpoints (for research-only purpose) at: https://github.com/ tienthanhdhcn/vnape .