Chitranuvad: Adapting Multi-lingual LLMs for Multimodal Translation

AbstractIn this work, we provide the system description of our submission as part of the English-to-Lowres Multimodal Translation Task at theWorkshop on Asian Translation (WAT2024). We introduce Chitranuvad, a multimodal model that effectively integrates Multilingual LLMand a vision module for Multimodal Translation. Our method uses a ViT image encoder to extract visual representations as visual tokenembeddings which are projected to the LLM space by an adapter layer and generates translation in an autoregressive fashion. We participated in all the three tracks (Image Captioning, Text-only and Multimodal translationtasks) for Indic languages (ie. English translation to Hindi, Bengali and Malyalam) and achieved SOTA results for Hindi in all of themon the Challenge set while remaining competitive for the other languages in the shared task.