@inproceedings{aralikatte-etal-2021-far,
title = "How far can we get with one {GPU} in 100 hours? {C}o{AS}ta{L} at {M}ulti{I}ndic{MT} Shared Task",
author = "Aralikatte, Rahul and
Murrieta Bello, H{\'e}ctor Ricardo and
de Lhoneux, Miryam and
Hershcovich, Daniel and
Bollmann, Marcel and
S{\o}gaard, Anders",
booktitle = "Proceedings of the 8th Workshop on Asian Translation (WAT2021)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.wat-1.24",
doi = "10.18653/v1/2021.wat-1.24",
pages = "205--211",
abstract = "This work shows that competitive translation results can be obtained in a constrained setting by incorporating the latest advances in memory and compute optimization. We train and evaluate large multilingual translation models using a single GPU for a maximum of 100 hours and get within 4-5 BLEU points of the top submission on the leaderboard. We also benchmark standard baselines on the PMI corpus and re-discover well-known shortcomings of translation systems and metrics.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="aralikatte-etal-2021-far">
<titleInfo>
<title>How far can we get with one GPU in 100 hours? CoAStaL at MultiIndicMT Shared Task</title>
</titleInfo>
<name type="personal">
<namePart type="given">Rahul</namePart>
<namePart type="family">Aralikatte</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Héctor</namePart>
<namePart type="given">Ricardo</namePart>
<namePart type="family">Murrieta Bello</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Miryam</namePart>
<namePart type="family">de Lhoneux</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Daniel</namePart>
<namePart type="family">Hershcovich</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Marcel</namePart>
<namePart type="family">Bollmann</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Anders</namePart>
<namePart type="family">Søgaard</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2021-aug</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 8th Workshop on Asian Translation (WAT2021)</title>
</titleInfo>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Online</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>This work shows that competitive translation results can be obtained in a constrained setting by incorporating the latest advances in memory and compute optimization. We train and evaluate large multilingual translation models using a single GPU for a maximum of 100 hours and get within 4-5 BLEU points of the top submission on the leaderboard. We also benchmark standard baselines on the PMI corpus and re-discover well-known shortcomings of translation systems and metrics.</abstract>
<identifier type="citekey">aralikatte-etal-2021-far</identifier>
<identifier type="doi">10.18653/v1/2021.wat-1.24</identifier>
<location>
<url>https://aclanthology.org/2021.wat-1.24</url>
</location>
<part>
<date>2021-aug</date>
<extent unit="page">
<start>205</start>
<end>211</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T How far can we get with one GPU in 100 hours? CoAStaL at MultiIndicMT Shared Task
%A Aralikatte, Rahul
%A Murrieta Bello, Héctor Ricardo
%A de Lhoneux, Miryam
%A Hershcovich, Daniel
%A Bollmann, Marcel
%A Søgaard, Anders
%S Proceedings of the 8th Workshop on Asian Translation (WAT2021)
%D 2021
%8 aug
%I Association for Computational Linguistics
%C Online
%F aralikatte-etal-2021-far
%X This work shows that competitive translation results can be obtained in a constrained setting by incorporating the latest advances in memory and compute optimization. We train and evaluate large multilingual translation models using a single GPU for a maximum of 100 hours and get within 4-5 BLEU points of the top submission on the leaderboard. We also benchmark standard baselines on the PMI corpus and re-discover well-known shortcomings of translation systems and metrics.
%R 10.18653/v1/2021.wat-1.24
%U https://aclanthology.org/2021.wat-1.24
%U https://doi.org/10.18653/v1/2021.wat-1.24
%P 205-211
Markdown (Informal)
[How far can we get with one GPU in 100 hours? CoAStaL at MultiIndicMT Shared Task](https://aclanthology.org/2021.wat-1.24) (Aralikatte et al., WAT 2021)
ACL