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.
翻译:机械翻译研究(MT)在几个领域取得了重要的突破。虽然为了扩大这一成功,还有许多工作要做,但我们认为,语言产业需要更好的方法来充分利用目前的成就。由于时间、资源和技能等各种因素的结合,企业倾向于将实用主义运用到其AI工作流程中。因此,企业更注重成果,如交付、运输、释放和特点,并尽可能采用高水平的工作生产解决方案。我们认为对笔译员有用的特征包括判决和字级翻译自动推荐和自动完成。建议替代方法可以激励翻译,限制他们参考外部资源的需求,希望外部资源能提高他们的生产力。这项工作描述了我们向WMT提交的文字层面自动完成共同任务,用于中文到英文、英文到中文、德文、英文到英文和英文到德文方向。我们研究使用预先培训模式和现有图书馆的外部框特征的可能性。我们采用随机抽样方法,以产生多种替代方法,希望外部资源能提高他们的生产力。我们向WMTMT的提交文件,用于字级自动化的自动完成。此外,我们采用基于开放版本的升级、升级和自动版本。