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
- Editors:
- Reza Haffari, Colin Cherry, George Foster, Shahram Khadivi, Bahar Salehi
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 33–42
- Language:
- URL:
- https://aclanthology.org/W18-3405
- DOI:
- 10.18653/v1/W18-3405
- 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., ACL 2018)
- PDF:
- https://preview.aclanthology.org/add_acl24_videos/W18-3405.pdf