@inproceedings{vardasbi-etal-2023-state,
title = "State Spaces Aren{'}t Enough: Machine Translation Needs Attention",
author = "Vardasbi, Ali and
Pires, Telmo Pessoa and
Schmidt, Robin and
Peitz, Stephan",
editor = "Nurminen, Mary and
Brenner, Judith and
Koponen, Maarit and
Latomaa, Sirkku and
Mikhailov, Mikhail and
Schierl, Frederike and
Ranasinghe, Tharindu and
Vanmassenhove, Eva and
Vidal, Sergi Alvarez and
Aranberri, Nora and
Nunziatini, Mara and
Escart{\'\i}n, Carla Parra and
Forcada, Mikel and
Popovic, Maja and
Scarton, Carolina and
Moniz, Helena",
booktitle = "Proceedings of the 24th Annual Conference of the European Association for Machine Translation",
month = jun,
year = "2023",
address = "Tampere, Finland",
publisher = "European Association for Machine Translation",
url = "https://aclanthology.org/2023.eamt-1.20",
pages = "205--216",
abstract = "Structured State Spaces for Sequences (S4) is a recently proposed sequence model with successful applications in various tasks, e.g. vision, language modelling, and audio. Thanks to its mathematical formulation, it compresses its input to a single hidden state, and is able to capture long range dependencies while avoiding the need for an attention mechanism. In this work, we apply S4 to Machine Translation (MT), and evaluate several encoder-decoder variants on WMT{'}14 and WMT{'}16. In contrast with the success in language modeling, we find that S4 lags behind the Transformer by approximately 4 BLEU points, and that it counter-intuitively struggles with long sentences. Finally, we show that this gap is caused by S4{'}s inability to summarize the full source sentence in a single hidden state, and show that we can close the gap by introducing an attention mechanism.",
}
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%0 Conference Proceedings
%T State Spaces Aren’t Enough: Machine Translation Needs Attention
%A Vardasbi, Ali
%A Pires, Telmo Pessoa
%A Schmidt, Robin
%A Peitz, Stephan
%Y Nurminen, Mary
%Y Brenner, Judith
%Y Koponen, Maarit
%Y Latomaa, Sirkku
%Y Mikhailov, Mikhail
%Y Schierl, Frederike
%Y Ranasinghe, Tharindu
%Y Vanmassenhove, Eva
%Y Vidal, Sergi Alvarez
%Y Aranberri, Nora
%Y Nunziatini, Mara
%Y Escartín, Carla Parra
%Y Forcada, Mikel
%Y Popovic, Maja
%Y Scarton, Carolina
%Y Moniz, Helena
%S Proceedings of the 24th Annual Conference of the European Association for Machine Translation
%D 2023
%8 June
%I European Association for Machine Translation
%C Tampere, Finland
%F vardasbi-etal-2023-state
%X Structured State Spaces for Sequences (S4) is a recently proposed sequence model with successful applications in various tasks, e.g. vision, language modelling, and audio. Thanks to its mathematical formulation, it compresses its input to a single hidden state, and is able to capture long range dependencies while avoiding the need for an attention mechanism. In this work, we apply S4 to Machine Translation (MT), and evaluate several encoder-decoder variants on WMT’14 and WMT’16. In contrast with the success in language modeling, we find that S4 lags behind the Transformer by approximately 4 BLEU points, and that it counter-intuitively struggles with long sentences. Finally, we show that this gap is caused by S4’s inability to summarize the full source sentence in a single hidden state, and show that we can close the gap by introducing an attention mechanism.
%U https://aclanthology.org/2023.eamt-1.20
%P 205-216
Markdown (Informal)
[State Spaces Aren’t Enough: Machine Translation Needs Attention](https://aclanthology.org/2023.eamt-1.20) (Vardasbi et al., EAMT 2023)
ACL
- Ali Vardasbi, Telmo Pessoa Pires, Robin Schmidt, and Stephan Peitz. 2023. State Spaces Aren’t Enough: Machine Translation Needs Attention. In Proceedings of the 24th Annual Conference of the European Association for Machine Translation, pages 205–216, Tampere, Finland. European Association for Machine Translation.