GWLAN : General Word-Level A utocompletio N for Computer-Aided Translation (2021)

Abstract: Computer-aided translation (CAT), the use of software to assist a human translator in the translation process, has been proven to be useful in enhancing the productivity of human translators. Autocompletion, which suggests translation results according to the text pieces provided by human translators, is a core function of CAT. There are two limitations in previous research in this line. First, most research works on this topic focus on sentence-level autocompletion (i.e., generating the whole translation as a sentence based on human input), but word-level autocompletion is under-explored so far. Second, almost no public benchmarks are available for the autocompletion task of CAT. This might be among the reasons why research progress in CAT is much slower compared to automatic MT. In this paper, we propose the task of general word-level autocompletion (GWLAN) from a real-world CAT scenario, and construct the first public benchmark to facilitate research in this topic. In addition, we propose an effective method for GWLAN and compare it with several strong baselines. Experiments demonstrate that our proposed method can give significantly more accurate predictions than the baseline methods on our benchmark datasets.

## introduction machine translation (mt) has witnessed great advancements with the emergence of neural machine translation (nmt) @xcite @xcite @xcite , which is able to produce much higher quality translation results than statistical machine translation (smt) models @xcite ; koehn, 1 the information of benchmark datasets is in https: //github.com/ghrua/gwlan we asked two sp ## related work computer-aided translation (cat) is a widely used practice when using mt technology in the industry. as the the mt systems advanced and improved, various efficient interaction ways of cat have emerged @xcite @xcite @xcite . among those different methods, the autocompletion is the most related to our work. therefore, we will first describe previous works in both sentence-level and word-level autocompletion, then show the relation to other tasks and scenarios. sentence-level autocompletion most of previous work in autocompletion for cat focus on sentence-level completion. a common use case in this line is interactive machine translation (imt) @xcite @xcite @xcite . imt systems utilize mt systems to complete the rest of a translation after human translators editing a prefix translation @xcite . for most imt systems, the core to achieve this completion is prefix-constrained decoding @xcite . another sentence-level autocompletion method, lexically constrained decoding (lcd) @xcite , recently attracts lots of attention @xcite @xcite . compared with imt, lcd relaxes the constraints provided by human translators from prefixes to general forms: lcd completes a translation based on some unordered words (i.e., lexical constraints), which are not necessary to be continuous @xcite @xcite . although it does not need additional training, its inference is typically less efficient compared with the standard nmt. therefore, other works propose efficient methods @xcite by using lexical constraints in a soft manner rather than a hard manner as in lcd. word-level autocompletion word-level autocompletion for cat is less studied than sentencelevel autocompletion. @xcite ; @xcite consider to complete a target word based on human typed characters and a translation prefix. but they require the target word to be the next word of the translation prefix, which limits its application. in contrast, in our work the proposed word-level autocompletion is more general and can be applied to real-world scenarios such as post-editing @xcite and lcd, where human translators need to input some words (corrections or constraints). @xcite propose a method to predict a target word based on human typed characters, however, this method only uses the source side information and does not consider translation context, leading to limited performance compared with our work. others our work may also be related to previous works in input method editors (ime) @xcite . however, they are in the monolingual setting and not capable of using the useful multilingual information. ## task and benchmark in this section, we first describe why we need wordlevel autocompletion in real-world cat scenarios. we then present the details of the gwlan task and the construction of benchmark. why gwlan? word level autocompletion is beneficial for improving input efficiency @xcite . previous works assume that the translation context should be a prefix and the desired word is next to the prefix as shown in figure 1 (b), where the context is "we asked two" and the desired word is "specialists". however, in some real-world cat scenarios such as post-editing and lexically constrained decoding, translation context may be discontinuous and the input words (corrections or lexical constraints) are not necessarily conjunct to the translation context. as shown in figure 1 (c), the context is "we • • • their opinion" and the human typed characters "sp" is conjunct to neither "we" nor "their" in the context. therefore, existing methods can not perform well on such a general scenario. this motivates us to propose a general word-level autocompletion task for cat. ## proposed approach given a tuple (x, c, s), our approach decomposes the whole word autocompletion process into two parts: model the distribution of the target word w based on the source sequence x and the translation context c, and find the most possible word w based on the distribution and human typed sequence s. therefore, in the following subsections, we firstly propose a word prediction model (wpm) to define the distribution p(w|x, c) of the target word w ( §4.1). then we can treat the human input sequence s as soft constraints or hard constraints to complete s and obtain the target word w ( §4.2). finally, we present two strategies for training and inference ( §4.3). ## conclusion we propose a general word-level autocomple-tion (gwlan) task for computer-aided translation (cat). in our setting, we relax the strict constraints on the translation contexts in previous work, and abstract four most general translation contexts used in real-world cat scenarios. we propose two approaches to address the variety of context types and weak position information issues in gwlan. to support automatic evaluation and to ensure a convenient and fair comparison among different methods, we construct a benchmark for the task. experiments on this benchmark show that our method outperforms baseline methods by a large margin on four datasets. we believe that this benchmark to be released will push forward future research in cat.