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。

计算机辅助翻译中单词级自动补全的重新思考

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

A wide range of applications play an important role in the daily work of the
modern human translator. However, the computational tools designed to aid in
the process of translation only benefit translation from or to a small minority
of the 7,000 languages of the world, those that we may call "privileged
languages". As for those translators who work with the remaining languages, the
marginalized languages in the digital world, they cannot benefit from the tools
that are speeding up the production of translation in the privileged languages.
We may ask whether it is possible to bridge the gap between what is available
for these languages and for the marginalized ones. This paper proposes a
framework for computer-assisted translation into marginalized languages and its
implementation in a web application for Spanish-Guarani translation. The
proposed system is based on a new theory for phrase-level translation in
contexts where adequate bilingual corpora are not available: Translation by
Generalized Segments (referred to as Minimal Dependency Translation in previous
work).

本文提出了一种基于 “广义片段翻译” 理论的计算机辅助翻译框架，并通过其在西班牙语 - 瓜拉尼语翻译中实现的 Web 应用程序，为边缘化语言的计算机翻译架起了一座桥梁。