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How far can we get ...
How far can we get with one GPU in 100 hours? CoAStaL at MultiIndicMT Shared Task
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Aralikatte, Rahul (författare)
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Murrieta Bello, Héctor Ricardo (författare)
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de Lhoneux, Miryam (författare)
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Hershcovich, Daniel (författare)
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- Bollmann, Marcel (författare)
- Jönköping University,Jönköping AI Lab (JAIL)
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Søgaard, Anders (författare)
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(creator_code:org_t)
- Stroudsburg, PA, USA : Association for Computational Linguistics, 2021
- 2021
- Engelska.
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Ingår i: Proceedings of the 8th Workshop on Asian Translation (WAT2021). - Stroudsburg, PA, USA : Association for Computational Linguistics. ; , s. 205-211
- Relaterad länk:
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https://aclanthology...
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https://aclanthology...
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Språkteknologi (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Language Technology (hsv//eng)
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)