Word-Level Auto-Completion (WLAC) plays a crucial role in Computer-Assisted
Translation. It aims at providing word-level auto-completion suggestions for
human translators. 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 answer 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.

通过研究不同的自动补全模型架构，本文提出了一种新的可衡量的标准，并发现现有的单词级自动补全模型往往无法满足此标准。基于此观察，我们提出了一种有效的方法来提高 WLAC 性能，同时利用较小的模型尺寸。