论文标题
具有明确的跨语性模式的零击翻译质量估计
Zero-Shot Translation Quality Estimation with Explicit Cross-Lingual Patterns
论文作者
论文摘要
本文介绍了我们在句子级别直接评估，质量估计（QE）上提交的WMT 2020共享任务。在这项研究中，我们从经验上揭示了\ textIt {不匹配问题}直接对量化量子的采用。具体而言，源句子和翻译的候选句子之间存在许多不匹配的错误，具有令牌成对的相似性。为了应对这个问题，我们建议将明确的跨语性模式曝光，\ textit {e.g。}单词比对和生成得分，将其与我们提出的零摄像模型。实验表明，我们提出的具有明确的跨语性模式的量化宽松模型可以减轻不匹配的问题，从而改善性能。令人鼓舞的是，我们的零量量化宽松方法可以通过有监督的量化量化量化方法实现可比的性能，甚至在6个方向中的2个方向上超过了受监督的对应物。我们希望我们的工作能够阐明零击量化宽松模型的改进。
This paper describes our submission of the WMT 2020 Shared Task on Sentence Level Direct Assessment, Quality Estimation (QE). In this study, we empirically reveal the \textit{mismatching issue} when directly adopting BERTScore to QE. Specifically, there exist lots of mismatching errors between the source sentence and translated candidate sentence with token pairwise similarity. In response to this issue, we propose to expose explicit cross-lingual patterns, \textit{e.g.} word alignments and generation score, to our proposed zero-shot models. Experiments show that our proposed QE model with explicit cross-lingual patterns could alleviate the mismatching issue, thereby improving the performance. Encouragingly, our zero-shot QE method could achieve comparable performance with supervised QE method, and even outperforms the supervised counterpart on 2 out of 6 directions. We expect our work could shed light on the zero-shot QE model improvement.
