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Multimodal neural machine translation for low-resource language pairs using synthetic data

Dutta Chowdhury, Koel, Hasanuzzaman, Mohammed orcid logoORCID: 0000-0003-1838-0091 and Liu, Qun orcid logoORCID: 0000-0002-7000-1792 (2018) Multimodal neural machine translation for low-resource language pairs using synthetic data. In: Workshop on Deep Learning Approaches for Low-Resource NLP, 19 July 2018, Melbourne, Australia.

In this paper, we investigate the effectiveness of training a multimodal neural machine translation (MNMT) system with image features for a lowresource language pair, Hindi and English, using synthetic data. A threeway parallel corpus which contains bilingual texts and corresponding images is required to train a MNMT system with image features. However, such a corpus is not available for low resource language pairs. To address this, we developed both a synthetic training dataset and a manually curated development/test dataset for Hindi based on an existing English-image parallel corpus. We used these datasets to build our image description translation system by adopting state-of-theart MNMT models. Our results show that it is possible to train a MNMT system for low-resource language pairs through the use of synthetic data and that such a system can benefit from image features.
Item Type:Conference or Workshop Item (Paper)
Event Type:Workshop
Subjects:Computer Science > Machine translating
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Institutes and Centres > ADAPT
Published in: Proceedings of the Workshop on Deep Learning Approaches for Low-Resource NLP. . Association for Computational Linguistics (ACL).
Publisher:Association for Computational Linguistics (ACL)
Official URL:https://www.aclweb.org/anthology/W18-3405
Copyright Information:© 2018 Association for Computational Linguistics (ACL)
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:ADAPT Centre for Digital Content Technology is founded under the SFI Research Centres Programme (Grant 13/RC/2106) and is co-funded under the European Regional Development Fund.
ID Code:23355
Deposited On:24 May 2019 15:11 by Thomas Murtagh . Last Modified 04 Jan 2021 16:59

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