GWLAN: General Word-Level AutocompletioN for Computer-Aided Translation

TL;DR

This paper introduces GWLAN, a new task and benchmark for word-level autocompletion in computer-aided translation, demonstrating a novel method that outperforms existing baselines.

Contribution

The paper defines the first public benchmark for word-level autocompletion in CAT and proposes an effective method that improves prediction accuracy.

Findings

Proposed method significantly outperforms baselines

Constructed the first public benchmark for GWLAN

Demonstrated effectiveness in real-world CAT scenarios

Abstract

Computer-aided translation (CAT), the use of software to assist a human translator in the translation process, has been proven to be useful in enhancing the productivity of human translators. Autocompletion, which suggests translation results according to the text pieces provided by human translators, is a core function of CAT. There are two limitations in previous research in this line. First, most research works on this topic focus on sentence-level autocompletion (i.e., generating the whole translation as a sentence based on human input), but word-level autocompletion is under-explored so far. Second, almost no public benchmarks are available for the autocompletion task of CAT. This might be among the reasons why research progress in CAT is much slower compared to automatic MT. In this paper, we propose the task of general word-level autocompletion (GWLAN) from a real-world CAT scenario, and construct the first public benchmark to facilitate research in this topic. In addition, we propose an effective method for GWLAN and compare it with several strong baselines. Experiments demonstrate that our proposed method can give significantly more accurate predictions than the baseline methods on our benchmark datasets.