Online Versus Offline NMT Quality: An In-depth Analysis on English–German and German–English

October 26, 2020

Abstract

We conduct in this work an evaluation study comparing offline and online neural machine translation architectures. Two sequence-to-sequence models: convolutional Pervasive Attention (Elbayad et al., 2018) and attention-based Transformer (Vaswani et al., 2017) are considered. We investigate, for both architectures, the impact of online decoding constraints on the translation quality through a carefully designed human evaluation on English-German and German-English language pairs, the latter being particularly sensitive to latency constraints. The evaluation results allow us to identify the strengths and shortcomings of each model when we shift to the online setup.

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AUTHORS

Written by

Jakob Verbeek

Emmanuelle Esperança-Rodier

Francis Brunet Manquat

Laurent Besacier

Maha Elbayad

Michael Ustaszewski

Publisher

COLING

Research Topics

Speech & Audio

Natural Language Processing (NLP)

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