CONF Parida_MMTLRL-2021_2021/IDIAP Multimodal Neural Machine Translation System for English to Bengali Parida, Shantipriya Panda, Subhadarshi Biswal, Satya Prakash Kotwal, Ketan Sen, Arghyadeep Dash, Satya Ranjan Motlicek, Petr http://publications.idiap.ch/index.php/publications/showcite/Parida_Idiap-RR-13-2021 Related documents Proceedings of the First Workshop on Multimodal Machine Translation for Low Resource Languages (MMTLRL 2021) Online (Virtual Mode) 2021 INCOMA Ltd. 31--39 https://aclanthology.org/2021.mmtlrl-1.6 URL Multimodal Machine Translation (MMT) systems utilize additional information from other modalities beyond text to improve the quality of machine translation (MT). The additional modality is typically in the form of images. Despite proven advantages, it is indeed difficult to develop an MMT system for various languages primarily due to the lack of a suitable multimodal dataset. In this work, we develop an MMT for English-> Bengali using a recently published Bengali Visual Genome (BVG) dataset that contains images with associated bilingual textual descriptions. Through a comparative study of the developed MMT system vis-a-vis a Text-to-text translation, we demonstrate that the use of multimodal data not only improves the translation performance improvement in BLEU score of +1.3 on the development set, +3.9 on the evaluation test, and +0.9 on the challenge test set but also helps to resolve ambiguities in the pure text description. As per best of our knowledge, our English-Bengali MMT system is the first attempt in this direction, and thus, can act as a baseline for the subsequent research in MMT for low resource languages. REPORT Parida_Idiap-RR-13-2021/IDIAP Multimodal Neural Machine Translation System for English to Bengali Parida, Shantipriya Panda, Subhadarshi Biswal, Satya Prakash Kotwal, Ketan Sen, Arghyadeep Dash, Satya Ranjan Motlicek, Petr Low resource language Machine Translation Multimodal machine translation EXTERNAL http://publications.idiap.ch/attachments/reports/2021/Parida_Idiap-RR-13-2021.pdf PUBLIC Idiap-RR-13-2021 2021 Idiap September 2021 Multimodal Machine Translation (MMT) systems utilize additional information from other modalities beyond text to improve the quality of machine translation (MT). The additional modality is typically in the form of images. Despite proven advantages, it is indeed difficult to develop an MMT system for various languages primarily due to the lack of a suitable multimodal dataset. In this work, we develop an MMT for English-> Bengali using a recently published Bengali Visual Genome (BVG) dataset that contains images with associated bilingual textual description. Through a comparative study of the developed MMT system vis-a-vis a Text-to-text translation, we demonstrate that the use of multimodal data not only improves the translation performance improvement in BLEU score of +1.3 on the development set, +3.9 on the evaluation test, and +0.9 on the challenge test set but also helps to resolve ambiguities in the pure text description. As per best of our knowledge, our English-Bengali MMT system is the first attempt in this direction, and thus, can act as a baseline for the subsequent research in MMT for low resource languages.