Existing translation models are typically trained on sentence-level and formal text, limiting their ability to capture everyday conversational dialogue phenomena such as informality, speaker interaction, and discourse coherence. Most existing Indic translation resources and evaluation benchmarks focus on sentence-level or formal text, making it difficult to assess translation quality of dialogue phenomena. In this work, we introduce BaatCheet, a multilingual dialogue corpus named after the Hindi term for conversation or chitchat, containing approximately 49,000 dialogues for dialogue translation across five translation directions. We fine-tune five open-source LLMs across seven training data configurations and find that fine-tuning yields substantial gains over zero- and few-shot baselines. To comprehensively evaluate dialogue translation quality, we employ multiple evaluation strategies, including automatic metrics, LLM-as-judge, and human assessments using an SQM-guided Direct Assessment (DA) Protocol.

We present our submission to the WMT 2026 Shared Task on Low-Resource Indic Language Translation, covering four language pairs (English-Assamese, English-Mizo, English-Khasi, and English-Tagin in both directions) spanning a wide range of resource levels, from moderately-resourced Assamese down to Tagin, which barely appears in existing multilingual MT systems at all. We compare three models (NLLB-200-1.3B, Sarvam-Translate, and TranslateGemma) across three setups: fine-tuning on the official shared-task data alone, fine-tuning with added external parallel data, and a reinforcement-learning stage (RLOO) that optimizes a combined BLEU/chrF++ reward on top of the fine-tuned model. Adding external parallel data helps most for Khasi, improving English-to-Khasi by 18 BLEU, and for Mizo, where our system places first among contrastive submissions in both directions; the same augmentation leaves Assamese essentially unchanged despite contributing twice as many sentence pairs, suggesting that data provenance matters more than volume at these scales. The RLOO system degrades performance on Assamese and Mizo, where supervised fine-tuning had already produced competitive systems, but improves over the primary system on both Khasi directions and on every metric for English-to-Tagin, where it obtains the highest METEOR score of any contrastive submission. Reward-based training therefore appears most useful precisely where supervised fine-tuning has least to work with.