Bilingual Synchronization: Restoring Translational Relationships with Editing Operations

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 8016 - 8030 December 7-11, 2022 ©2022 Association for Computational Linguistics Bilingual Synchronization: Restoring Translational Relationships with Editing Operations Jitao Xu† Josep Crego‡ François Yvon† †Université Paris-Saclay, CNRS, LISN, 91400, Orsay, France ‡SYSTRAN, 5 rue Feydeau, 75002, Paris, France {jitao.xu,francois.yvon}@limsi.fr, josep.crego@systrangroup.com Abstract Machine Translation (MT) is usually viewed as a one-shot process that generates the tar- get language equivalent of some source text from scratch. We consider here a more gen- eral setting which assumes an initial target se- quence, that must be transformed into a valid translation of the source, thereby restoring parallelism between source and target. For this bilingual synchronization task, we con- sider several architectures (both autoregressive and non-autoregressive) and training regimes, and experiment with multiple practical settings such as simulated interactive MT, translating with Translation Memory (TM) and TM clean- ing. Our results suggest that one single generic edit-based system, once fine-tuned, can com- pare with, or even outperform, dedicated sys- tems specifically trained for these tasks. 1 Introduction Neural Machine Translation (NMT) systems have made tangible progress in recent years (Bahdanau et al., 2015; Vaswani et al., 2017), as they started to produce usable translations in production environ- ments. NMT is generally viewed as a one-shot ac- tivity process in autoregressive approaches, which generates the target translation based on the sole source side input. Recently, Non-autoregressive Machine Translation (NAT) models have proposed to perform iterative refinement decoding (Lee et al., 2018; Ghazvininejad et al., 2019; Gu et al., 2019), where translations are generated through an itera- tive revision process, starting with a possibly empty initial hypothesis. This paper focuses on the revision part of the ma- chine translation (MT) process and consider bilin- gual synchronization (Bi-sync), which we define as follows: given a pair of a source (f) and a target (˜e) sentences, which may or may not be mutual translations, the task is to compute a revised ver- sion e of ˜e, such that e is an actual translation of f. This is necessary when the source side of an ex- isting translation is edited, requiring to update the target and keep both sides synchronized. Bi-sync subsumes standard MT, where the synchronization starts with an empty target (˜e = []). Other interest- ing cases occur when parts of the initial target can be reused, so that the synchronization only requires a few changes. Bi-sync encompasses several tasks: synchroniza- tion is needed in interactive MT (IMT, Knowles and Koehn, 2016) and bilingual editing (Bronner et al., 2012), with ˜e the translation of a previous version of f; in MT with lexical constraints (Hokamp and Liu, 2017), where ˜e contains target-side constraints (Susanto et al., 2020; Xu and Carpuat, 2021); in Translation Memory (TM) based approaches (Bulte and Tezcan, 2019), where ˜e is a TM match for a similar example; in automatic post-editing (APE) (do Carmo et al., 2021), where ˜e is an MT output. We consider here several implementations of sequence-to-sequence models …

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