Speeding up inference time of neural machine translation – Bc. Martin Geletka
Bc. Martin Geletka
Master's thesis
Speeding up inference time of neural machine translation
Speeding up inference time of neural machine translation
Abstract:
Vďaka takzvaným Transformer modelom sa nedávno dosiahli signifikantné posuny v úlohách strojového prekladu. V praxi však tieto modely trpia vysokou latenciou, takže sú často nepoužiteľné v praktických aplikáciách. Táto práca študuje dôvody tejto vysokej latencie a zhrnuje, aplikuje a porovnáva techniky strojového učenia, ktorých cieľom je skrátenie inferenčného času Transformer modelov použitých pri …moreAbstract:
Large qualitative gains were recently made in machine translation tasks thanks to Transformers models. However, in practice, these models suffer from high latency, such that they often are hardly usable in practical applications. This thesis study studies the reasons behind high latency time and tries to overview, employ and compare techniques, which tries to decrease the inference time of the Transformer …more
Language used: English
Date on which the thesis was submitted / produced: 14. 12. 2021
Identifier:
https://is.muni.cz/th/if8vh/
Thesis defence
- Date of defence: 1. 2. 2022
- Supervisor: doc. RNDr. Petr Sojka, Ph.D.
- Reader: RNDr. Vít Novotný
Citation record
Full text of thesis
Contents of on-line thesis archive
Published in Theses:- světu
Other ways of accessing the text
Institution archiving the thesis and making it accessible: Masarykova univerzita, Fakulta informatikyMasaryk University
Faculty of InformaticsMaster programme / field:
Artificial intelligence and data processing / Machine learning and artificial intelligence
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