61 citations · 61 across the 2 of their papers we have counts for
6 papers
Online Versus Offline NMT Quality: An In-depth Analysis on English-German and German-English
Maha Elbayad, Michael Ustaszewski, Emmanuelle Esperança-Rodier +3
We conduct in this work an evaluation study comparing offline and online neural machine translation architectures. Two sequence-to-sequence models: convolutional Pervasive Attentio…
ON-TRAC Consortium for End-to-End and Simultaneous Speech Translation Challenge Tasks at IWSLT 2020
Maha Elbayad, Ha Nguyen, Fethi Bougares +5
This paper describes the ON-TRAC Consortium translation systems developed for two challenge tracks featured in the Evaluation Campaign of IWSLT 2020, offline speech translation and…
Efficient Wait-k Models for Simultaneous Machine Translation
Maha Elbayad, Laurent Besacier, Jakob Verbeek
Simultaneous machine translation consists in starting output generation before the entire input sequence is available. Wait-k decoders offer a simple but efficient approach for thi…
Depth-Adaptive Transformer
Maha Elbayad, Jiatao Gu, Edouard Grave +1
State of the art sequence-to-sequence models for large scale tasks perform a fixed number of computations for each input sequence regardless of whether it is easy or hard to proces…
Pervasive Attention: 2D Convolutional Neural Networks for Sequence-to-Sequence Prediction
Maha Elbayad, Laurent Besacier, Jakob Verbeek
Current state-of-the-art machine translation systems are based on encoder-decoder architectures, that first encode the input sequence, and then generate an output sequence based on…
Token-level and sequence-level loss smoothing for RNN language models
Maha Elbayad, Laurent Besacier, Jakob Verbeek
Despite the effectiveness of recurrent neural network language models, their maximum likelihood estimation suffers from two limitations. It treats all sentences that do not match t…