# BaatCheet: A Multilingual Corpus for Dialogue Translation in Indian Languages

arXiv: https://arxiv.org/abs/2609.33296 · HTML: https://mlsanity.com/paper/2609.33296/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 37 references that are in the map.
- First posted on arXiv 27 Sep 2026.

## Builds on (most cited first, 18 of 18)

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- [DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset](https://mlsanity.com/paper/1710.03957/) (2017)
- [Prompting Large Language Model for Machine Translation: A Case Study](https://mlsanity.com/paper/2301.07069/) (2023)
- [A Rank Stabilization Scaling Factor for Fine-Tuning with LoRA](https://mlsanity.com/paper/2312.03732/) (2023)
- [IndicTrans2: Towards High-Quality and Accessible Machine Translation Models for all 22 Scheduled Indian Languages](https://mlsanity.com/paper/2305.16307/) (2023)
- [MuTual: A Dataset for Multi-Turn Dialogue Reasoning](https://mlsanity.com/paper/2004.04494/) (2020)
- [DailyTalk: Spoken Dialogue Dataset for Conversational Text-to-Speech](https://mlsanity.com/paper/2207.01063/) (2022)
- [Embarrassingly Easy Document-Level MT Metrics: How to Convert Any Pretrained Metric Into a Document-Level Metric](https://mlsanity.com/paper/2209.13654/) (2022)
- [Cross-Lingual Dialogue Dataset Creation via Outline-Based Generation](https://mlsanity.com/paper/2201.13405/) (2022)
- [Contextual Neural Model for Translating Bilingual Multi-Speaker Conversations](https://mlsanity.com/paper/1809.00344/) (2018)
- [Bhasha-Abhijnaanam: Native-script and romanized Language Identification for 22 Indic languages](https://mlsanity.com/paper/2305.15814/) (2023)
- [DiaBLa: A Corpus of Bilingual Spontaneous Written Dialogues for Machine Translation](https://mlsanity.com/paper/1905.13354/) (2019)
- [Automatic Construction of Discourse Corpora for Dialogue Translation](https://mlsanity.com/paper/1605.06770/) (2016)
- [Beyond Correlation: Interpretable Evaluation of Machine Translation Metrics](https://mlsanity.com/paper/2410.05183/) (2024)
- [Context-Aware LLM Translation System Using Conversation Summarization and Dialogue History](https://mlsanity.com/paper/2410.16775/) (2024)
- [Towards Personalised and Document-level Machine Translation of Dialogue](https://mlsanity.com/paper/2102.10979/) (2021)
- [Crosslingual Optimized Metric for Translation Assessment of Indian Languages](https://mlsanity.com/paper/2509.17667/) (2025)

## Abstract (arXiv)

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 the 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.