Multimodal Neural Machine Translation for Low-resource Language Pairs using Synthetic Data
Koel Dutta Chowdhury, Mohammed Hasanuzzaman, Qun Liu · 2018
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.