Understanding Pre-Editing for Black-Box Neural Machine Translation

Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics, pages 1539–1550 April 19 - 23, 2021. ©2021 Association for Computational Linguistics 1539 Understanding Pre-Editing for Black-Box Neural Machine Translation Rei Miyata† Atsushi Fujita‡ †Nagoya University, Nagoya, Japan ‡National Institute of Information and Communications Technology, Kyoto, Japan miyata@nuee.nagoya-u.ac.jp, atsushi.fujita@nict.go.jp Abstract Pre-editing is the process of modifying the source text (ST) so that it can be translated by machine translation (MT) in a better quality. Despite the unpredictability of black-box neu- ral MT (NMT), pre-editing has been deployed in various practical MT use cases. Although many studies have demonstrated the effective- ness of pre-editing methods for particular set- tings, thus far, a deep understanding of what pre-editing is and how it works for black-box NMT is lacking. To elicit such understanding, we extensively investigated human pre-editing practices. We first implemented a protocol to incrementally record the minimum edits for each ST and collected 6,652 instances of pre- editing across three translation directions, two MT systems, and four text domains. We then analysed the instances from three perspectives: the characteristics of the pre-edited ST, the di- versity of pre-editing operations, and the im- pact of the pre-editing operations on NMT out- puts. Our findings include the following: (1) enhancing the explicitness of the meaning of an ST and its syntactic structure is more im- portant for obtaining better translations than making the ST shorter and simpler, and (2) al- though the impact of pre-editing on NMT is generally unpredictable, there are some ten- dencies of changes in the NMT outputs de- pending on the editing operation types. 1 Introduction Recent advances in machine translation (MT) have greatly facilitated its practical use in various set- tings from business documentation to personal communication. In many practical cases, MT sys- tems are used as black-box and one well-tested approach to make use of a black-box MT is pre- editing, i.e., modifying the source text (ST) to make it suitable for the intended MT system. The effectiveness of pre-editing has so far been demonstrated in many studies (Pym, 1990; O’Brien and Roturier, 2007; Seretan et al., 2014). A study focusing on statistical MT (SMT) has also shown that more than 90% of an ST can be rewritten into a text that can be machine-translated with sufficient quality (Miyata and Fujita, 2017), exhibiting the potential of the pre-editing approach. However, the feasibility and possibility of pre- editing for neural MT (NMT) has not been ex- amined extensively. While efforts have recently been invested in the implementation of pre-editing strategies for black-box NMT settings, achieving improved MT quality (e.g., Hiraoka and Yamada, 2019; Mehta et al., 2020), the potential gains of pre- editing remain unexplored. Notably, the impact of pre-editing on black-box MT is unpredictable in nature. In particular, NMT models trained in an end-to-end manner can be sensitive to minor modi- fications of the ST (Cheng et al., 2019), which may affect the feasibility of pre-editing. In short, while pre-editing has been …

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