Neural Machine Translation

Marta Ruiz will present his work on GAN applied to Speech Enhancement.
May 18th, at 12.
Telefonica i+d (Plaça d'Ernest Lluch i Martin, 5, 08019 Barcelona)




Neural Machine Translation (MT) is starting to become a standard both in industry and in academics. The new paradigm is entirely based on an end-to-end deep learning architecture .




Neural MT was proposed in 2014 using a simple sequence-to-sequence architecture. This architecture has evolved introducing bidirectional recurrent neural networks, attention-based mechanism, convolutional networks and multi-task training. Several of these advances have been applied to other tasks such as image/video captioning or sentence entailment.




In this talk, we will explain the neural MT architecture from its basis to the recent proposals. We will review Google and Systran’s systems in production as well as top systems presented in the popular international evaluation campaign of WMT showing the good performance of the system compared to state-of-the-art statistical/rule-based MT systems.  







Marta R. Costa-jussà is a Telecommunication Engineer by the Universitat Politècnica de Catalunya (UPC, Barcelona). She received her PhD from the UPC in 2008. Her research experience is mainly in Machine Translation. She has worked at LIMSI-CNRS (Paris), Barcelona Media Innovation Center (Barcelona), Universidade de São Paulo, Institute for Infocomm Research (Singapore) and Instituto Politécnico Nacional (Mexico). Her research experience/results include: participation in 17 research projects; publication of over 100 papers in international journals/conferences; cooperation with more than 5 companies as scientific consultant; organization of 12 workshops/conferences in the area.Currently, she is a Ramón y Cajal Research Fellow at UPC and she is leading the DeepVoice project.

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