Multimodal Neural Machine Translation for English to Hindi
Sahinur Rahman Laskar, Abdullah Faiz Ur Rahman Khilji, Partha Pakray, Sivaji Bandyopadhyay · 2020
Machine translation (MT) focuses on the automatic translation of text from one natural language to another natural language.Neural machine translation (NMT) achieves state-of-theart results in the task of machine translation because of utilizing advanced deep learning techniques and handles issues like long-term dependency, and context-analysis.Nevertheless, NMT still suffers low translation quality for low resource languages.To encounter this challenge, the multi-modal concept comes in.The multi-modal concept combines textual and visual features to improve the translation quality of low resource languages.Moreover, the utilization of monolingual data in the pre-training step can improve the performance of the system for low resource language translations.Workshop on Asian Translation 2020 (WAT2020) organized a translation task for multimodal translation in English to Hindi.We have participated in the same in two-track submission, namely text-only and multi-modal translation with team name CNLP-NITS.The evaluated results are declared at the WAT2020 translation task, which reports that our multimodal NMT system attained higher scores than our text-only NMT on both challenge and evaluation test set.For the challenge test data, our multi-modal neural machine translation system achieves Bilingual Evaluation Understudy (BLEU) score of 33.57, Rankbased Intuitive Bilingual Evaluation Score (RIBES) 0.754141, Adequacy-Fluency Metrics (AMFM) score 0.787320 and for evaluation test data, BLEU, RIBES, and, AMFM score of 40.51, 0.803208, and 0.820980 for English to Hindi translation respectively.