Word Level Auto Completion

Word-level auto-completion (WLAC) in computer-aided translation aims to suggest the next word a human translator might type, boosting efficiency and quality. Recent research focuses on improving the accuracy of these suggestions, particularly for low-frequency words, by developing more sophisticated models that effectively leverage contextual information from both the source and target languages, including the partially-typed word itself. This involves exploring various neural network architectures, including non-autoregressive approaches and energy-based models, and refining criteria for evaluating the quality of auto-completion suggestions. Improved WLAC systems have the potential to significantly enhance the productivity and accuracy of human translators.