Previous research for adapting a general neural machine translation (NMT) model into a specific domain usually neglects the diversity in translation within the same domain, which is a core problem for domain adaptation in real-world scenarios. One representative of such challenging scenarios is to deploy a translation system for a conference with a specific topic, e.g., global warming or coronavirus, where there are usually extremely less resources due to the limited schedule. To motivate wider investigation in such a scenario, we present a real-world fine-grained domain adaptation task in machine translation (FGraDA). The FGraDA dataset consists of Chinese-English translation task for four sub-domains of information technology: autonomous vehicles, AI education, real-time networks, and smart phone. Each sub-domain is equipped with a development set and test set for evaluation purposes. To be closer to reality, FGraDA does not employ any in-domain bilingual training data but provides bilingual dictionaries and wiki knowledge base, which can be easier obtained within a short time. We benchmark the fine-grained domain adaptation task and present in-depth analyses showing that there are still challenging problems to further improve the performance with heterogeneous resources.
翻译:以往将一般神经机器翻译(NMT)模式改造为特定领域的研究通常忽视同一领域的翻译多样性,这是现实世界情景中领域适应工作的一个核心问题。这种具有挑战性的情景的一名代表是为会议配置一个翻译系统,会议有一个具体议题,如全球变暖或冠状病毒,由于时间有限,其资源通常极少。为了鼓励在这种情景中进行更广泛的调查,我们提出了一个在机器翻译中进行真实世界精细区分的域适应任务(FGRADA)。FGRADA数据集由四个信息技术子领域(自主车辆、AI教育、实时网络和智能电话)的中文-英文翻译任务组成。每个子领域都配备了一套用于评估目的的开发装置和测试装置。为了更接近现实,FGRADA没有使用任何内部的双语培训数据,而是提供双语词典和wiki知识库,这在很短的时间内可以更容易获得。我们为精细的域适应任务设定了基准,目前进行的深入分析显示,在进一步改进业绩方面存在挑战性的问题。