5 citations · 9 across the 5 of their papers we have counts for
5 papers
Learning Language-Specific Layers for Multilingual Machine Translation
Telmo Pessoa Pires, Robin M. Schmidt, Yi-Hsiu Liao +1
Multilingual Machine Translation promises to improve translation quality between non-English languages. This is advantageous for several reasons, namely lower latency (no need to t…
State Spaces Aren't Enough: Machine Translation Needs Attention
Ali Vardasbi, Telmo Pessoa Pires, Robin M. Schmidt +1
Structured State Spaces for Sequences (S4) is a recently proposed sequence model with successful applications in various tasks, e.g. vision, language modeling, and audio. Thanks to…
Non-Autoregressive Neural Machine Translation: A Call for Clarity
Robin M. Schmidt, Telmo Pires, Stephan Peitz +1
Non-autoregressive approaches aim to improve the inference speed of translation models by only requiring a single forward pass to generate the output sequence instead of iterativel…
Jointly Learning to Align and Translate with Transformer Models
Sarthak Garg, Stephan Peitz, Udhyakumar Nallasamy +1
The state of the art in machine translation (MT) is governed by neural approaches, which typically provide superior translation accuracy over statistical approaches. However, on th…
Local System Voting Feature for Machine Translation System Combination
Markus Freitag, Jan-Thorsten Peter, Stephan Peitz +2
In this paper, we enhance the traditional confusion network system combination approach with an additional model trained by a neural network. This work is motivated by the fact tha…