Machine Translation with Unsupervised Length-Constraints

Abstract

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.

8 pages

References in corpus (5)

• Sequence to Sequence Learning with Neural Networks
• Toward Multilingual Neural Machine Translation with Universal Encoder and Decoder
• Adding Interpretable Attention to Neural Translation Models Improves Word Alignment
• Effective Strategies in Zero-Shot Neural Machine Translation
• Controlling Text Complexity in Neural Machine Translation