This paper presents the approach and results of team v036 in the English-to-Low-Resource Multi-Modal Translation Task at the Ninth Conference on Machine Translation (WMT24). Our team tackled the challenge of translating English source text to low-resource Indic languages, specifically Hindi, Malayalam, and Bengali, while leveraging visual context provided alongside the text data. We used InternVL2 for extracting the image context along with Knowledge Distillation from bigger LLMs to train Small Language Model on the tranlsation task. During current shared task phase, we submitted best models (for this task), and overall we got rank 3 on Hindi, Bengali, and Malyalam datasets. We also open source our models on huggingface.

In this paper describes the approach which we have built for causality extraction from the financial documents that we have submitted for FinCausal 2022 task 2. We proving a solution with intelligent pre-processing and post-processing to detect the number of cause and effect in a financial document and extract them. Our given approach achieved 90% as F1 score(weighted-average) for the official blind evaluation dataset.