Research on Machine Translation (MT) has achieved important breakthroughs in
several areas. While there is much more to be done in order to build on this
success, we believe that the language industry needs better ways to take full
advantage of current achievements. Due to a combination of factors, including
time, resources, and skills, businesses tend to apply pragmatism into their AI
workflows. Hence, they concentrate more on outcomes, e.g. delivery, shipping,
releases, and features, and adopt high-level working production solutions,
where possible. Among the features thought to be helpful for translators are
sentence-level and word-level translation auto-suggestion and auto-completion.
Suggesting alternatives can inspire translators and limit their need to refer
to external resources, which hopefully boosts their productivity. This work
describes our submissions to WMT's shared task on word-level auto-completion,
for the Chinese-to-English, English-to-Chinese, German-to-English, and
English-to-German language directions. We investigate the possibility of using
pre-trained models and out-of-the-box features from available libraries. We
employ random sampling to generate diverse alternatives, which reveals good
results. Furthermore, we introduce our open-source API, based on CTranslate2,
to serve translations, auto-suggestions, and auto-completions.

本研究利用预训练模型和随机抽样等方法，为机器翻译提供了句子和单词级别的自动翻译建议和自动补全功能，并且介绍了一种基于 CTranslate2 的翻译 API。

基于单词级别自动完成：开箱即用我们能取得什么成果？

Translation Word-Level Auto-Completion: What can we achieve out of the box?

In this work, we propose a joint system combining a talking face generation
system with a text-to-speech system that can generate multilingual talking face
videos from only the text input. Our system can synthesize natural multilingual
speeches while maintaining the vocal identity of the speaker, as well as lip
movements synchronized to the synthesized speech. We demonstrate the
generalization capabilities of our system by selecting four languages (Korean,
English, Japanese, and Chinese) each from a different language family. We also
compare the outputs of our talking face generation model to outputs of a prior
work that claims multilingual support. For our demo, we add a translation API
to the preprocessing stage and present it in the form of a neural dubber so
that users can utilize the multilingual property of our system more easily.

本论文提出一种多语言对话生成系统，结合了人脸生成和文本到语音系统，可以仅通过文本输入生成多种语言的自然语音、同步口型，无论输入文本的语言如何，均可维持语音人的声音特征。同时，我们添加了翻译 API，展示神经配音技术的应用。