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First Step toward Neural Machine Translation

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If you have a question about this talk, please contact Tamara Polajnar .

Neural machine translation is a recently introduced approach to tackling machine translation. Unlike the conventional approach, neural machine translation aims to build a translation system that consists of a single neural network, where the whole system is jointly trained to maximize the conditional probability of a translation given a source sentence. In this talk, I will first introduce a basic concept of neural machine translation and demonstrate the working neural translation system. Then, I will describe how we incorporated attention mechanism into an encoder-decoder model to achieve translation performance comparable to the conventional phrase-based machine translation system (http://arxiv.org/abs/1409.0473)

This talk is part of the NLIP Seminar Series series.

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In person in the Max Perutz Lecture Theatre (CB2 0QH) and via Zoom, link: https://mrc-lmb-cam-ac-uk.zoom.us/j/96608781341?pwd=MG5QMXVmOStXcFVtalBqUlBYc0tEdz09 Harker 1, Dept of Earth Sciences Dyson Seminar Room, Department of Engineering / Online (Zoom)

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