Rethinking Word-Level Auto-Completion in Computer-Aided Translation

Xingyu Chen, Lemao Liu, Guoping Huang, Zhirui Zhang, Mingming Yang, Shuming Shi, Rui Wang

Submission Type: Regular Long Paper

Submission Track: Machine Translation

Keywords: machine translation, interactive machine translation, computer assisted translation, word level auto completion

TL;DR: This paper propose a joint-training method to improve the performance of the word-level auto-completion task.

Abstract: Word-level auto-completion (WLAC) plays a crucial role in Computer-Assisted Translation. While previous studies have primarily focused on designing complex model architectures, this paper takes a different perspective by rethinking the fundamental question: what kind of words are good auto-completions? We introduce a measurable criterion to address this question and discover that existing WLAC models often fail to meet this criterion. Building upon this observation, we propose an effective approach to enhance WLAC performance by promoting adherence to the criterion. Notably, the proposed approach is general and can be applied to various encoder-based architectures. Through extensive experiments, we demonstrate that our approach outperforms the top-performing system submitted to the WLAC shared tasks in WMT2022, while utilizing significantly smaller model sizes.

Submission Number: 2343