Online Versus Offline NMT Quality: An In-depth Analysis on English-German and German-English
Maha Elbayad, Michael Ustaszewski, Emmanuelle Esperança-Rodier, Francis Brunet-Manquat, Jakob Verbeek, Laurent Besacier
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.
BibTeX
@inproceedings{elbayad-etal-2020-online,
title = "Online Versus Offline {NMT} Quality: An In-depth Analysis on {E}nglish-{G}erman and {G}erman-{E}nglish",
author = "Elbayad, Maha and
Ustaszewski, Michael and
Esperan{\c{c}}a-Rodier, Emmanuelle and
Brunet-Manquat, Francis and
Verbeek, Jakob and
Besacier, Laurent",
editor = "Scott, Donia and
Bel, Nuria and
Zong, Chengqing",
booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2020.coling-main.443/",
doi = "10.18653/v1/2020.coling-main.443",
pages = "5047--5058"
}