论文标题
DeepSubqe：字幕翻译的质量估计
DeepSubQE: Quality estimation for subtitle translations
论文作者
论文摘要
涉及语言数据的任务的质量估计（QE）很难归因于自然语言的许多方面，例如隔离，样式，语法等方面的变化。根据手头的应用，可以有多个答案，可接受性的含量不同。在这项工作中，我们研究了视频字幕的翻译质量。我们展示了现有的量化量化量化方法是如何不足的，并提出了我们的方法DeepSubqe作为一个系统，以估算一对语言的字幕数据的翻译质量。我们依靠各种数据增强策略来自动标签和培训合成。我们创建了一个混合网络，该网络可以学习双语数据的语义和句法特征，并将其与仅LSTM和CNN网络进行比较。我们提出的网络以显着的利润优于它们。
Quality estimation (QE) for tasks involving language data is hard owing to numerous aspects of natural language like variations in paraphrasing, style, grammar, etc. There can be multiple answers with varying levels of acceptability depending on the application at hand. In this work, we look at estimating quality of translations for video subtitles. We show how existing QE methods are inadequate and propose our method DeepSubQE as a system to estimate quality of translation given subtitles data for a pair of languages. We rely on various data augmentation strategies for automated labelling and synthesis for training. We create a hybrid network which learns semantic and syntactic features of bilingual data and compare it with only-LSTM and only-CNN networks. Our proposed network outperforms them by significant margin.
