Lexical Translation Inconsistency-Aware Document-Level Translation Repair (2023)

Abstract: Following the idea of “one translation per discourse”, in this paper we aim to improve translation consistency via document-level translation repair (DocRepair), i.e., automatic post-editing on translations of documents. To this end, we propose a lexical translation inconsistency-aware DocRepair to explicitly model translation inconsistency. First we locate the inconsistency in automatic translation. Then we provide translation candidates for those inconsistency. Finally, we propose lattice-like input to properly model inconsistent tokens and phrases and their candidates. Experimental results on three document-level translation datasets show that based on G-Transformer, a state-of-the-art document-to-document (Doc2Doc) translation model, our Doc2Doc DocRepair achieves significant improvement on translation quality in BLEU scores, but also greatly improves lexical translation consistency.

## introduction although neural machine translation (nmt) has made remarkable progress @xcite , sentence-level nmt still suffers from the serious problem of lexical translation inconsistency due to the lack of inter-sentence context. to better model inter-sentence context, previous studies in document-level nmt propose various context-aware models which use sentences in the wider-document context, thus implicitly learning discourse correlations as a by-product of optimising an nmt model @xcite . however, as these models rarely try to model discourse phenomena explicitly, there still exist much rooms for improvement on discourse phenomena. in this paper, we follow up the idea of "one translation per discourse" @xcite @xcite ; khotaba and tarawneh, #1: #13: ... ... #17: figure 1 shows an example of an input document and its translation from both state-of-the-art sentence-level and document-level nmt models. the source words like 孙燕姿/sun_yan_zi, 沙尘 暴/sha_chen_bao and 当地人/dang_di_ren, occurring two or more times within the source document, unexpectedly get different translations while they are translated consistently in its reference (human translation). for example, person name 孙 燕姿/sun_yan_zi is translated into sun yen-tzu and sun yanzi by sentence-level nmt. such inconsistent translations, however, tend to confuse readers. moreover, even some context-aware documentlevel nmt models like g-transformer @xcite could not well alleviate this phenomenon as shown in the figure. very few studies in document-level nmt explicitly encourage lexical translation consistency. @xcite obtain a word link for each source word in a document and exchange their context information in encoding by using an auxiliary loss to constrain their translation being consistent. @xcite and @xcite both construct source-side lexical chains, and use different approaches to learn (or model) translations for tokens within the same lexical chain. different from above studies which encourage translation consistency in the translation process, in this paper we aim to improve translation consistency via docrepair. different from @xcite which implicitly learns inconsistency within document translation, we propose a lexical translation inconsistencyaware docrepair model to explicitly correct translation inconsistency. given automatic translation t of a document s, either from sentence-level nmt or document-level nmt, this is done by the following steps. first, in translation t we locate inconsistent phrases, each of which consists of one or more consecutive tokens. then, we provide translation candidates for those inconsistent phrases. finally, we adapt g-transformer, a state-of-the-art document-to-document translation model, to repair document-level translation t equipped with inconsistent phrases and their candidates. overall, we make the following contributions. ## experimentation to verify the effectiveness of our proposed approach, we conduct experiments on three datasets with three language pairs, i.e., chinese-to-english (@xmath0, english-to-chinese (@xmath1. ## analysis next, we take nist @xmath2: number of translation candidates. ## related work the idea of "one translation per discourse" has been studied in both document-level translation and repair (i.e., post-editing). ## conclusion in this paper, we have proposed an inconsistencyaware docrepair approach to improve documentlevel translation consistency via automatic postediting. we first locate inconsistency in text translation and provide translation candidates for each inconsistency. then we use lattice-like input to properly model inconsistency and their candidates in a document-level repair model. experimental results on three document-level translation datasets show that our approach not only achieves improvement on translation quality in bleu, but also greatly improves lexical translation consistency. both pre-training and fine-tuning stage, we use early-stopping strategy with the patience as 10 and choose the best checkpoint according to the valid loss. the whole training process takes approximately 40 hours. in inference, we set the beam size to 5.