Document Type

Conference Paper

Rights

Available under a Creative Commons Attribution Non-Commercial Share Alike 4.0 International Licence

Disciplines

Computer Sciences, General language studies, Linguistics

Publication Details

In 2016 I was invited by the European Commission Directorate-General for Translation to present an tutorial on neural-machine translation at the ``Translating Europe Forum 2016: Focusing on Translation Technologies'' held in Brussels on the 27th and 28th October 2016. This paper is based on that tutorial. A video of the tutorial is available at: https://webcast. ec.europa.eu/translating-europe-forum-2016-jenk-1, the tutorial starts 2 hours into the video (timestamp 2 : 00 : 15) and runs for just over 15 minutes.

Abstract

This paper presents a short introduction to neural networks and how they are used for machine translation and concludes with some discussion on the current research challenges being addressed by neural machine translation (NMT) research. The primary goal of this paper is to give a no-tears introduction to NMT to readers that do not have a computer science or mathematical background. The secondary goal is to provide the reader with a deep enough understanding of NMT that they can appreciate the strengths of weaknesses of the technology. The paper starts with a brief introduction to standard feed-forward neural networks (what they are, how they work, and how they are trained), this is followed by an introduction to word-embeddings (vector representations of words) and then we introduce recurrent neural networks. Once these fundamentals have been introduced we then focus in on the components of a standard neural-machine translation architecture, namely: encoder networks, decoder language models, and the encoder-decoder architecture.

DOI

https://doi.org/10.21427/D78012

Funder

ADAPT Research Centre

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