论文标题
无监督的长度约束的机器翻译
Machine Translation with Unsupervised Length-Constraints
论文作者
论文摘要
由于深度学习的使用，我们已经看到机器翻译的显着改善。虽然翻译质量的改进令人印象深刻，但编码器架构构建可以实现更多可能性。在本文中，我们探讨了其中之一，即约束翻译的产生。我们专注于长度约束，如果应以给定格式显示翻译，这是必不可少的。在这项工作中，我们为此任务提出了一种端到端的方法。与首先翻译然后执行句子压缩的传统方法相比，完全不受监督的文本压缩。通过将IDEA与零拍的多语言机器翻译相结合，我们还可以执行无监督的单语句子压缩。为了满足长度约束，我们研究了几种将约束集成到模型中的方法。使用提出的技术，我们能够显着提高约束下的翻译质量。此外，我们能够执行无监督的单语句子压缩。
We have seen significant improvements in machine translation due to the usage of deep learning. While the improvements in translation quality are impressive, the encoder-decoder architecture enables many more possibilities. In this paper, we explore one of these, the generation of constraint translation. We focus on length constraints, which are essential if the translation should be displayed in a given format. In this work, we propose an end-to-end approach for this task. Compared to a traditional method that first translates and then performs sentence compression, the text compression is learned completely unsupervised. By combining the idea with zero-shot multilingual machine translation, we are also able to perform unsupervised monolingual sentence compression. In order to fulfill the length constraints, we investigated several methods to integrate the constraints into the model. Using the presented technique, we are able to significantly improve the translation quality under constraints. Furthermore, we are able to perform unsupervised monolingual sentence compression.
