SJTU - MTLAB ’s Submission to the WMT 23 Word-Level Auto Completion Task (2023)

Abstract: Word-level auto-completion (WLAC) plays a crucial role in Computer-Assisted Translation. In this paper, we describe the SJTU-MTLAB’s submission to the WMT23 WLAC task. We propose a joint method to incorporate the machine translation task to the WLAC task. The proposed approach is general and can be applied to various encoder-based architectures. Through extensive experiments, we demonstrate that our approach can greatly improve performance, while maintaining significantly small model sizes.

## introduction in recent years, more and more researchers have studied computer-aided translation (cat) that aims to assist human translators to translate the input text @xcite @xcite @xcite . the wordlevel auto-completion (wlac) task @xcite is the core function of cat, which involves predicting the word being typed by the translator given the translation context, as illustrated in figure 1 . effective auto-completion has the potential to reduce keystrokes by at least 60% during the translation process @xcite . a user survey indicates that 90.2% of participants find the word-level auto-suggestion feature helpful @xcite . therefore, wlac plays an important role in cat. there are many existing methods for modeling wlac, and they mainly differ in model architectures @xcite @xcite @xcite . for example, @xcite ; @xcite design a bert-like architecture to directly predict the target word while @xcite employ a model similar to the auto-regressive nmt to predict the bpe tokens of the target word. assume the human translator is going to input the golden translation. the auto-completion suggests the possible word candidates given the typed characters. it can be more accurate with the help of translation hypothesis from mt models. the wlac task comes from a real translation scenario: a human translator is translating a source sentence, who has already translated part of the sentence, and is typing a new word. the input contains three parts: the source sentence s, the partial translation c, and the typed sequence t. the wlac task is to predict the word w that the translator is going to input @xcite . rooted in the translation natural, we consider a fundamental question: what defines a correct word w? theoretically, a good w should appear in the reference translation, as illustrated in figure 1 . therefore, we attempt to incorporate knowledges from machine translation into the wlac task. we presents two novel approach to enhance wlac systems, called joint-inference and joint-training, to combine the mt task and the wlac task during inference and training, respectively. the effectiveness of our proposed method is validated through experiments conducted on the four language directions of the wlac shared task in wmt2023 ( §4). remarkably, our approach achieves substantial improvements across two distinct backbone models. ## backbone models for wlac in this section, we introduce two types of backbone models for the wlac task. these backbone models serve as the foundation for our proposed techniques and experiments in subsequent sections. ## enhancing wlac by incorporating mt task in this section, we propose two different approaches to improve the wlac task. ## conclusion this paper proposes an effective approach to improve wlac performance by combining the mt task and the wlac task. we inject the translation knowledge into the walc model by jointly train the two tasks. extensive experiments show that the proposed approach surpasses several strong baselines with much smaller model size.