Multimodal Neural Machine Translation for Low-resource Language Pairs using Synthetic Data

Koel Dutta Chowdhury, Mohammed Hasanuzzaman, Qun Liu


Abstract
In this paper, we investigate the effectiveness of training a multimodal neural machine translation (MNMT) system with image features for a low-resource language pair, Hindi and English, using synthetic data. A three-way 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-the-art 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.
Anthology ID:
W18-3405
Volume:
Proceedings of the Workshop on Deep Learning Approaches for Low-Resource NLP
Month:
July
Year:
2018
Address:
Melbourne
Venues:
ACL | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
33–42
Language:
URL:
https://aclanthology.org/W18-3405
DOI:
10.18653/v1/W18-3405
Bibkey:
Cite (ACL):
Koel Dutta Chowdhury, Mohammed Hasanuzzaman, and Qun Liu. 2018. Multimodal Neural Machine Translation for Low-resource Language Pairs using Synthetic Data. In Proceedings of the Workshop on Deep Learning Approaches for Low-Resource NLP, pages 33–42, Melbourne. Association for Computational Linguistics.
Cite (Informal):
Multimodal Neural Machine Translation for Low-resource Language Pairs using Synthetic Data (Dutta Chowdhury et al., 2018)
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PDF:
https://aclanthology.org/W18-3405.pdf