IN ar IG : Iterative Non-autoregressive Instruct Generation Model For Word-Level Auto Completion (2023)

Abstract: Computer-aided translation (CAT) aims to enhance human translation efficiency and is still important in scenarios where machine translation cannot meet quality requirements. One fundamental task within this field is Word-Level Auto Completion (WLAC). WLAC predicts a target word given a source sentence, translation context, and a human typed character sequence. Previous works either employ word classification models to exploit contextual information from both sides of the target word or directly disregarded the dependencies from the right-side context. Furthermore, the key information, i.e. human typed sequences, is only used as prefix constraints in the decoding module. In this paper, we propose the INarIG (Iterative Non-autoregressive Instruct Generation) model, which constructs the human typed sequence into Instruction Unit and employs iterative decoding with subwords to fully utilize input information given in the task. Our model is more competent in dealing with low-frequency words (core scenario of this task), and achieves state-of-the-art results on the WMT22 and benchmark datasets, with a maximum increase of over 10% prediction accuracy.

## introduction transformer and its variants @xcite @xcite , coupled with large-scale parallel and synthetic corpora @xcite @xcite , have boosted machine translation quality to a large extent. now, machine translation (mt) can basically meet users' requirements in scenarios where translation quality is undemanding, for instance, understanding social media posts. however, in other scenarios where audience expect highly accurate translations (e.g. reading a product manual or a government document), manual proofreading and post-editing are still required. re- ## methodology in this section, we first present the overall structure of our model. then, we introduce the instruction unit we used, as well as our decoding procedure. figure 2 shows the details. ## anlysis in this chapter, we analyzes the effectiveness of our innovative points and optimization strategies used in training to better validate our method. unless otherwise specified, the experiments are conducted on the benchmark dataset. ## discussion & future work two issues requires more research and analysis: ## conclusion in this paper, we propose a novel iterative nonautoregressive instruct generation model for the wlac task, and validate the effectiveness of our model on two mainstream datasets. subsequently, through carefully-designed experiments, we verify that our model's better capability of utilizing avaialbe information compared to previous works, as our approach uses model-based deep information fusion. we also demonstrate that our iterative decoding based on subwords can improve the accuracy of low-frequency words and better meet translators' requirements. afterwards, through comparative experiments, we confirm that a series of training strategies used in our work help improve accuracy.