PhD Topics

Adaptation of Neural Machine Translation

Guidelines

Neural methods have become the state-of-the-art in the field of machine translation and brought about significant improvements of translation quality in general-purpose translation. This thesis will focus on methods improving translation quality for specific purposes (e.g. search qeury translation) and/or domains (terminology translation). The explored techniques will include multilingual translation, query expansion through translation, exploting terminology dictionaries, phrase translations etc.

References

Goodfellow, I., Y. Bengio, and A. Courville 2016. Deep learning. Cambridge, MA, USA: MIT press.

Orhan Firat, Kyunghyun Cho, and Yoshua Bengio. Multi-way, multilingual neural machine translation with a shared attention mechanism. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 866–875, San Diego, California, June 2016. Association for Computational Linguistics.

Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. Attention is All you Need. In I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, editors, Advances in Neural Information Processing Systems 30, pages 6000–6010. Curran Associates, Inc., 2017.