Easy Guided Decoding in Providing Suggestions for Interactive Machine Translation (2023)

Abstract: Machine translation technology has made great progress in recent years, but it cannot guarantee error-free results. Human translators perform post-editing on machine translations to correct errors in the scene of computer aided translation. In favor of expediting the post-editing process, many works have investigated machine translation in interactive modes, in which machines can automatically refine the rest of translations constrained by human’s edits. Translation Suggestion (TS), as an interactive mode to assist human translators, requires machines to generate alternatives for specific incorrect words or phrases selected by human translators. In this paper, we utilize the parameterized objective function of neural machine translation (NMT) and propose a novel constrained decoding algorithm, namely Prefix-Suffix Guided Decoding (PSGD), to deal with the TS problem without additional training. Compared to state-of-the-art lexical-constrained decoding method, PSGD improves translation quality by an average of 10.6 BLEU and reduces time overhead by an average of 63.4% on benchmark datasets. Furthermore, on both the WeTS and the WMT 2022 Translation Suggestion datasets, it is superior over other supervised learning systems trained with TS annotated data.

## introduction and related work the emergence of machine translation technology @xcite assists human translation to improve translation efficiency @xcite @xcite . even though there is a quality gap between the outputs of machine translation (mt) systems and manual translations by professional translators, mt can still practically reduce time in comparison with translating from scratch @xcite . later, the advances at the sequence-to-sequence model @xcite @xcite further made a breakthrough in translation technology, inspiring the industry to transform human translation into computer-aided translation (cat) @xcite to a great extent. cat usually relies on an mt engine and a platform with a user-friendly interface @xcite @xcite @xcite , with which humans perform postediting (pe) on machine translations to achieve final results with quality standards. in the past, the post-editing process was typically static and machines would no longer respond to humans' modifications once humans started postediting. recent works @xcite @xcite investigate interactive protocols and algorithms so that humans and machines can collaborate and machines automatically refine the translations according to the human's edits. one promising mode is translation suggestion (ts) proposed by @xcite as a pioneer, which requires the machine to provide alternatives for specific spans of incorrect words or phrases selected by humans, namely making suggestions given prefix and suffix constraints. in practical applications as shown in figure 1 , it usually happens when human translators would like to edit part of the mt output. it can be easily implemented with a user interface if machines correctly provide suggestions for the selected incorrect spans. @xcite has proven the significance of ts in post-editing in terms of resolving two pitfalls of earlier works. the importance has also been recognized by the conference of machine translation (wmt), and they released the naive translation example 1 source ressortchef robert gates hatte am vortag erklärt, er nehme den kritischen bericht des rechnungshofes im kongress sehr ernst und erwäge verschiedene möglichkeiten. translation defense secretary robert gates said the day before that he took the critical report by the court of auditors in congress very seriously and considered various options. suggestion for the span head of department final translation head of department robert gates said the day before that he took the critical report by the court of auditors in congress very seriously and considered various options. example 2 source er soll ende 2008 für die dauer von zweieinhalb jahren bestimmt werden und die eu in aller welt repräsentieren. (cp/dpa) translation at the end of 2008, it is to be appointed for two and a half years and represent the eu around the world (cp / dpa). ## preliminary before introducing the psgd, we first elaborate on the problem in the scene of ts with the mathematical notations in given training pairs of (x, y), maximizing the probability in formula 1 will return an estimate of the model parameter, θ. during inference, we are supposed to maximize the probability of the generated sequence: however, the solution space {y} is infinite and impossible to search. therefore, an auto-regressive decoding process is applied as an approximation. the decoder of the model decodes the translation step by step greedily foot_3 as following until eos is met: in the scene of ts, a span r is masked for suggestion generation, and thus we need to guarantee that y starts with the given prefix p and ends with the given suffix s. then, the problem becomes: as mentioned previously, earlier works of constrained decoding require step-by-step generations for the whole sentence even with prefix and suffix constraints. it causes efficiency issues especially when the constraints are long. what's worse, the division of beams might bring about low-quality suggestions. accordingly, psgd generates translation suggestions given prefix and suffix constraints through maximizing the probability of the whole generated sequence. meanwhile, psgd performs a step-by-step beam search generation only for the span without dividing beams. a brief example of the psgd algorithm for ts can be found in figure 2 . with this picture in mind, we will give details of the psgd algorithm in this section. the pseudo codes of this algorithm are presented in algorithm 1. and the fairseq based implementation is available on github foot_4 . ## conclusion in this paper, we propose a neat prefix-suffix guided decoding (psgd) algorithm for the translation suggestion task in computer-aided translation. it emphasizes the probability of the entire generation including prefix and suffix constraints and decodes only for incorrect spans selected by human translators with an early stopping mechanism. given a pre-trained auto-regressive nmt model, psgd can be easily applied during inference without any additional training/fine-tuning. comprehensive experimental results demonstrate that psgd significantly outperforms the state-of-the-art constrained decoding algorithm, vdba, in all of the translation quality, time efficiency, and robustness. meanwhile, it is also superior to supervised ts systems trained with human-annotated data.