Autocorrect in the Process of Translation — Multi-task Learning Improves Dialogue Machine Translation (2021)

Abstract: Automatic translation of dialogue texts is a much needed demand in many real life scenarios. However, the currently existing neural machine translation delivers unsatisfying results. In this paper, we conduct a deep analysis of a dialogue corpus and summarize three major issues on dialogue translation, including pronoun dropping (), punctuation dropping (), and typos (). In response to these challenges, we propose a joint learning method to identify omission and typo, and utilize context to translate dialogue utterances. To properly evaluate the performance, we propose a manually annotated dataset with 1,931 Chinese-English parallel utterances from 300 dialogues as a benchmark testbed for dialogue translation. Our experiments show that the proposed method improves translation quality by 3.2 BLEU over the baselines. It also elevates the recovery rate of omitted pronouns from 26.09% to 47.16%. We will publish the code and dataset publicly at https://xxx.xx.

## introduction remarkable progress has been made in neural machine translation (nmt) @xcite @xcite in recent years, which has been widely applied in everyday life. a typical scenario for such application is translating dialogue texts, in particular the record of group chats or movie subtitles, which helps people of different languages understand cross-language chat and improve their comprehension capabilities. however, traditional nmt models translate texts in a sentence-by-sentence manner and focus on the formal text input, such as wmt news translation @xcite , while the translation of dialogue must take the meaning of context and the input noise into account. despite its vast potential application, efforts of exploration into dialogue translation are far from enough. existing works @xcite focus on either extracting dialogues from parallel corpora, such as opensubtitles @xcite , or leveraging speaker information for integrating dialogue context into neural models. also, the lack of both training data and benchmark test set makes current dialogue translation models far from satisfying and need to be further improved. in this paper, we try to alleviate the aforementioned challenges in dialogue translation. we first analyze a fraction of a dialogue corpus and summarize three critical issues in dialogue translation, including prodrop, pundrop, and dialtypo. then we design a multi-task learning (mtldial) approach that learns to self-correct sentences in the process of translating. the model's encoder part automatically learns how to de-noise the noise input via explicit supervisory signals provided by additional contextual labeling. we also propose three strong baselines for dialogue translation, including repair (repairdial) and robust (robustdial) model. to alleviate the challenges arising from the scarcity of dialogue data, we use sub-documents in the bilingual parallel corpus to enable the model to learn from crosssentence context. additionally as for evaluation, the most commonly used bleu metric @xcite for nmt is not good enough to provide a deep look into the translation quality in such a scenario. thus, we build a chinese-english test set containing sentences with the issues in prodrop, pundrop and dialtypo, attached with the human translation and annotation. finally, we get a test set of 300 dialogues with 1,931 parallel sentences. the main contributions of this paper are as follows: a) we analyze three challenges prodrop, pundrop and dialtypo, which greatly impact the understanding and translation of a dialogue. b) we propose a contextual multi-task learning method to tackle the analyzed challenges. c) we create a chinese-english test set specifically containing those problems and conduct experiments to evaluate proposed method on this test set. ## analysis on dialogue translation there were already some manual analyses of translation errors, especially in the field of discourse translation. @xcite study english-russian translation and find three main challenges for discourse translation: deixis, ellipsis, and lexical cohesion. for chinese-english translation, tense consistency, connective mismatch, and content-heavy sentences are the most common issues @xcite . different from previous works, we mainly analyze the specific phenomena in dialogue translation. we begin with a study on a bilingual dialogue corpus @xcite . 1 we translate source sentences into the target language at sentence level and compare translation results with reference at dialogue level. around 1,000 dialogues are evaluated, and the results are reported in ## analysis 5.1 labeling performance to better understand how our proposed mtldial make sense, we calculate the labeling performance on both validation and test set. ## conclusions in this paper, we manually analyze challenges in dialogue translation and detect three main problems. in order to tackle these issues, we propose a multitask learning method with contextual labeling. for deep evaluation, we construct dialogues with translation and detailed annotations as a benchmark test set. our proposed model achieves substantial improvements over the baselines. what is more, we further analyze the performance of contextual labeling and pronoun recovery errors.