Translating short segments with NMT: a case study in English-to-Hindi

• Autores: Shantipriya Parida, Ondřej Bojar
• Localización: Proceedings of the 21st Annual Conference of the European Association for Machine Translation: 28-30 May 2018, Universitat d'Alacant, Alacant, Spain / coord. por Juan Antonio Pérez Ortiz, Felipe Sánchez Martínez, Miquel Esplà Gomis, Maja Popovic, Celia Rico Pérez, André Martins, Joachim Van den Bogaert, Mikel L. Forcada Zubizarreta, 2018, ISBN 978-84-09-01901-4, págs. 229-238
• Idioma: inglés
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• 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.