Translating Short Segments with NMT: A Case Study in English-to-Hindi
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Título: | Translating Short Segments with NMT: A Case Study in English-to-Hindi |
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Autor/es: | Parida, Shantipriya | Bojar, Ondřej |
Palabras clave: | Machine Translation |
Área/s de conocimiento: | Lenguajes y Sistemas Informáticos |
Fecha de publicación: | 2018 |
Editor: | European Association for Machine Translation |
Cita bibliográfica: | Parida, Shantipriya; Bojar, Ondřej. “Translating Short Segments with NMT: A Case Study in English-to-Hindi”. In: Pérez-Ortiz, Juan Antonio, et al. (Eds.). Proceedings of the 21st Annual Conference of the European Association for Machine Translation: 28-30 May 2018, Universitat d'Alacant, Alacant, Spain, pp. 229-238 |
Resumen: | This paper presents a case study in translating short image captions of the Visual Genome dataset from English into Hindi using out-of-domain data sets of varying size. We experiment with three NMT models: the shallow and deep sequence-to-sequence and the Transformer model as implemented in Marian toolkit. Phrase-based Moses serves as the baseline. The results indicate that the Transformer model outperforms others in the large data setting in a number of automatic metrics and manual evaluation, and it also produces the fewest truncated sentences. Transformer training is however very sensitive to the hyperparameters, so it requires more experimenting. The deep sequence-to-sequence model produced more flawless outputs in the small data setting and it was generally more stable, at the cost of more training iterations. |
Patrocinador/es: | This work has been supported by the grants 18-24210S of the Czech Science Foundation, SVV 260 453 and “Progress” Q18+Q48 of Charles University, and using language resources distributed by the LINDAT/CLARIN project of the Ministry of Education, Youth and Sports of the Czech Republic (projects LM2015071 and OP VVV VI CZ.02.1.01/0.0/0.0/16 013/0001781). |
URI: | http://hdl.handle.net/10045/76083 |
ISBN: | 978-84-09-01901-4 |
Idioma: | eng |
Tipo: | info:eu-repo/semantics/conferenceObject |
Derechos: | © 2018 The authors. This article is licensed under a Creative Commons 3.0 licence, no derivative works, attribution, CC-BY-ND. |
Revisión científica: | si |
Versión del editor: | http://eamt2018.dlsi.ua.es/proceedings-eamt2018.pdf |
Aparece en las colecciones: | EAMT2018 - Proceedings |
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