In this paper, we describe our system under the team name BLEU Monday for the English-to-Indic Multimodal Translation Task at WAT 2025. We participate in the text-only translation tasks for English-Hindi, English-Bengali, English-Malayalam, and English-Odia language pairs. We present a two-stage approach that addresses quality issues in the training data through automated error detection and correction, followed by parameter-efficient model fine-tuning.Our methodology introduces a vision-augmented judge-corrector pipeline that leverages multimodal language models to systematically identify and correct translation errors in the training data. The judge component classifies translations into three categories: correct, visually ambiguous (requiring image context), or mistranslated (poor translation quality). Identified errors are routed to specialized correctors: GPT-4o-mini regenerates captions requiring visual disambiguation, while IndicTrans2 retranslates cases with pure translation quality issues. This automated pipeline processes 28,928 training examples across four languages, correcting an average of 17.1% of captions per language.We then apply Low-Rank Adaptation (LoRA) to fine-tune the IndicTrans2 en-indic 200M distilled model on both original and corrected datasets. Training on corrected data yields consistent improvements, with BLEU score gains of +1.30 for English-Bengali on the evaluation set (42.00 → 43.30) and +0.70 on the challenge set (44.90 → 45.60), +0.60 for English-Odia on the evaluation set (41.00 → 41.60), and +0.10 for English-Hindi on the challenge set (53.90 → 54.00).

The IIT-CDIP document collection is the source of several widely used and publicly accessible document understanding datasets. In this paper, manual inspection of 5 datasets derived from IIT-CDIP uncovers the presence of thousands of instances of sensitive personal data, including US Social Security Numbers (SSNs), birth places and dates, and home addresses of individuals. The presence of such sensitive personal data in commonly-used and publicly available datasets is startling and has ethical and potentially legal implications; we believe such sensitive data ought to be removed from the internet. Thus, in this paper, we develop a modular data de-identification pipeline that replaces sensitive data with synthetic, but realistic, data. Via experiments, we demonstrate that this de-identification method preserves the utility of the de-identified documents so that they can continue be used in various document understanding applications. We will release redacted versions of these datasets publicly.

In this paper, we describe our system under the team name Brotherhood for the English-to-Lowres Multi-Modal Translation Task. We participate in the multi-modal translation tasks for English-Hindi, English-Hausa, English-Bengali, and English-Malayalam language pairs. We present a method leveraging multi-modal Large Language Models (LLMs), specifically GPT-4o and Claude 3.5 Sonnet, to enhance cross-lingual image captioning without traditional training or fine-tuning.Our approach utilizes instruction-tuned prompting to generate rich, contextual conversations about cropped images, using their English captions as additional context. These synthetic conversations are then translated into the target languages. Finally, we employ a weighted prompting strategy, balancing the original English caption with the translated conversation to generate captions in the target language.This method achieved competitive results, scoring 37.90 BLEU on the English-Hindi Challenge Set and ranking first and second for English-Hausa on the Challenge and Evaluation Leaderboards, respectively. We conduct additional experiments on a subset of 250 images, exploring the trade-offs between BLEU scores and semantic similarity across various weighting schemes.