BaatCheet: A Multilingual Corpus for Dialogue Translation in Indian Languages

Priyanka Dasari; Yuvrajsinh D. Bodana; Vandan Mujadia; Arafat Ahsan; Dipti Misra Sharma; Parameswari Krishnamurthy

cs.CLSep 27, 2026

BaatCheet: A Multilingual Corpus for Dialogue Translation in Indian Languages

Authors: Priyanka Dasari, Yuvrajsinh D. Bodana, Vandan Mujadia, Arafat Ahsan, Dipti Misra Sharma, Parameswari Krishnamurthy

Organizations: Language Technologies Research Centre, IIIT Hyderabad, India

Abstract

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.

Figures & tables

Figure 1: Highlighting Linguistic phenomena in informal Telugu dialogue. Dashed boxes highlight examples of pro-drop, ellipses, and register-specific informal expressions in a sample conversation.

Figure 2: A flowchart of the BaatCheet Corpora preparation process for training and evaluation. The Metric-based methodology for selecting high-quality synthetic is detailed in Section 3.3 .

Figure 3: Baseline performance comparison across nine candidate systems on BaatCheet_Human_Train using (a)reference-based COMET and (b)reference-free COMET-QE. Dedicated MT systems ( GMT , IndicTrans2 , BhashaVerse ) and GPT-4o-mini consistently outperform open-source LLMs across all language directions.

BaatCheet Corpus	#Dialogues	#Utterances	#Sentences		Avg Dlg Len	Avg Utt Len		Avg Sent Len
BaatCheet Corpus	#Dialogues	#Utterances	Src	Tgt	Avg Dlg Len	Src	Tgt	Src	Tgt
BaatCheet_Syn_X2IL	42,148	301,278	533,102	533,494	8.8	14.8	11.8	8.7	6.9
BaatCheet_Syn_IL2X	6,096	73,666	135,894	136,616	12.3	14.6	11.8	8.4	6.9
BaatCheet_Human_Train	510	8,405	16,455	16,077	17.3	17.6	14.1	9.1	7.5
BaatCheet_Test	658	8,904	16,813	16,518	13.7	16.9	14.1	8.9	7.5
TOTAL	49,412	392,253	702,264	702,705	13.0	16.0	13.0	8.8	7.2

Table 1: Overall statistics of the BaatCheet corpus across 4 splits.

	Baselines		Synthetic-Only		Human-Only	Dual-Hybrid		Multi-Hybrid
Model	Zero-Shot	Few-Shot	Syn_X2IL	Syn_IL2X	HT	Syn_X2IL+HT	Syn_IL2X+HT	Syn_Both+HT	Syn_Both+3 $\times$ HT
(a) COMET (Reference-Based)
Gemma3-4B	0.5024	0.6410	0.8076	0.7095	0.8324	0.8192	0.8223	0.7397	0.6087
Llama-3.2-3B	0.5317	0.5864	0.7802	0.7279	0.7139	0.7410	0.7076	0.7647	0.7271
Llama-3.1-8B	0.7113	0.6882	0.8023	0.7418	0.7871	0.7197	0.7210	0.7930	0.8461
Qwen3-4B	0.6797	0.6601	0.8373	0.7372	0.7959	0.8063	0.7370	0.7695	0.8288

Table 2: Performance comparison of five open-source LLMs across fine-tuning configurations on BaatCheet_Test , evaluated using (a) COMET DA, (b) DOC-COMET, and (c) COMTAIL DA. Syn_Both denotes Syn_[X2IL+IL2X] and HT denotes Human_Train . Bold values indicate the best-performing data configuration for each respective model.

		GPT Judge				Gemini Judge				Human Judge
		Utt-Level	Dia-Level Factors			Utt-Level	Dia-Level Factors			Utt-Level	Dia-Level Factors
Model	Configuration	Acc	Nat	Inf	Coh	Acc	Nat	Inf	Coh	Acc	Nat	Inf	Coh
Gemma	Syn_X2IL+HT	48.58	42.79	48.82	46.66	47.94	45.95	48.86	47.69	49.98	46.45	49.63	47.46
Gemma	Human_Train	50.31	48.99	50.52	50.92	50.54	51.62	52.25	52.79	51.52	50.20	50.15	50.67
Llama-3B	Syn_X2IL+HT	43.14	39.55	46.76	37.47	41.31	37.49	41.81	37.13	43.22	43.11	49.23	42.31
Llama-3B	Syn_X2IL	44.41	43.64	41.54	39.82	43.96	41.95	40.78	40.50	43.35	44.88	46.25	43.91

Table 3: Qualitative SQM-DA evaluation scores (

z

-score normalized and scaled) disaggregated by Utterance-Level Accuracy (Utt-level) and Dialogue-Level Factors (Dia-level): Naturalness ( Nat ), Informality ( Inf ), and Cohesion & Coherence ( Coh ). Evaluated across top-two data configurations per model by GPT-4o-mini, Gemini-Flash-Lite-preview, and Human Judges. Syn_Both denotes Syn_[X2IL+IL2X] . Bold text indicates the higher score per model setup.

Figure 4: Average translation performance (mean score across all five fine-tuned models and three evaluation metrics: COMET, DOC-COMET, and COMTAIL) for each fine-tuned training configuration.

	GPT vs H		Ge vs Hu		GPT vs Ge
Factor	$r$	$\tau$	$r$	$\tau$	$r$	$\tau$
Nat	0.552	0.370	0.625	0.428	0.738	0.540
Inf	0.343	0.232	0.332	0.209	0.391	0.266
Coh	0.561	0.364	0.647	0.442	0.743	0.532
Utt	0.702	0.498	0.697	0.525	0.781	0.597

Table 4: Average inter-judge correlation (

r

= Pearson,

\tau

= Kendall’s

\tau

) across all five models. Bold values indicate the highest agreement factor per judge pair(GPT, Gemini- Ge and Human- H . Utterance-Level Accuracy (Utt-level) and Dialogue-Level Factors (Dia-level): Naturalness ( Nat ), Informality ( Inf ), and Cohesion & Coherence ( Coh ). Ge .

Appendix figures & tables21 assets

Supplementary material from the paper’s appendix.

Appendix

Figure 5: An example of Formal and Informal Translation from English to Hindi along with translitration in IAST format. All the differences are highlited with colors.

Data Source – Src. Lang	Description
Hinglish-Chat – Hin	A synthetically generated Hinglish conversational dataset Khatri (2024) featuring everyday-life dialogues. The dataset is modified by LLM prompting to “Remove unnatural noun and pronoun usage from this dialogue, but it still has to make sense and write it in Hindi (Devanagari) script.”
MuTual (Multi-Turn Dialogue Reasoning) – Eng	A Chinese high school English listening comprehension dataset Cui et al. (2020) , adapted, preprocessed, and manually annotated to create a multi-turn dialogue reasoning corpus aimed at evaluating dialogue models’ contextual understanding and reasoning capabilities.
DailyTalk – Eng	A conversational speech dataset for TTS systems developed by sampling and modifying dialogues from the open-domain DailyDialog corpus Li et al. (2017) . Dialogues from DailyTalk Lee et al. (2022) are used here, annotated with speaker identifiers to capture natural conversational flow.
News Articles – Eng, Hin, Tam, Tel	News articles Mujadia and Sharma (2025) from diverse domains are used to generate informal dialogues using LLM prompting: “Generate 10 informal dialogues between two friends on this topic. Keep them short, natural, conversational and direct, and conclude meaningfully.”
Short Stories – Tam, Tel	Short stories from online sources such as tamil_stories 12 12 12 https://huggingface.co/datasets/aitamilnadu/tamil_stories for Tamil and the Chandamama Kathalu Dataset 13 13 13 https://code.swecha.org/telugu-ai/chandamama-kathalu-dataset for Telugu are used. Each story is prompted as: “You are given a [language] story text. Generate one informal dialogue between two [language] friends discussing the story in detail in a natural, code-mixed (with English) tone in [language]. The dialogue should be conversational and informal like spoken language. Reach an opinion about the story.”

Appendix

Table 5: Overview of dialogue data sources used for constructing BaatCheet Corpora across English, Hindi, Tamil, and Telugu.

Datasource	Total Dialogues	Edited Dialogues	Dialogue Edit Rate (%)↓	Total Utts	Edited Utts	Utt Edit Rate(%)↓	TER↓ (%)	CER↓ (%)
News-Eng †	50	4	8.00	516	4	0.78	0.31	0.25
Hinglish †	49	49	100.00	967	638	65.98	21.99	19.60
News-Hin †	49	34	69.39	492	139	28.25	3.96	2.77
News-Tam	48	27	56.25	344	61	17.73	4.81	2.94
News-Tel	48	38	79.17	361	167	46.26	17.87	11.91
Stories-Tam	50	48	96.00	777	330	42.47	9.68	6.21

Appendix

Table 6: Edit metrics of Human Validation across all data sources. † Indicates BaatCheet_Gold source dialogues; remaining splits are sampled from trainset splits. We report Dialogue Edit Rate, Utterance(Utt) Edit Rate, Token Edit Rate (TER), and Character Edit Rate (CER). Lower is better.

Direction	Data Source	#Dialogues	#Utterances	#Sentences (Src / Tgt)	Avg Dlg Len	Avg Utt Len (Src / Tgt)	Avg Sent Len (Src / Tgt)
(a) BaatCheet_Syn_X2IL ( $\text{X}\rightarrow\text{IL}$ Direction: English / Hindi $\longrightarrow$ Indic Language)
Eng $\rightarrow$ Hin	DailyTalk	2,446	22,152	32,582 / 32,312	9.06	11.28 / 11.41	7.81 / 7.94
	MuTual	8,346	38,332	68,560 / 68,594	4.59	16.35 / 17.56	9.29 / 9.93
	News	826	7,834	12,690 / 12,663	9.48	11.86 / 13.39	7.34 / 8.30
	Total	11,618	68,318	113,832 / 113,569	5.88	14.97 / 15.97	8.84 / 9.39
Eng $\rightarrow$ Tam	DailyTalk	2,446	22,152	32,582 / 32,585	9.06	11.28 / 8.52	7.81 / 5.90

Appendix

Table 7: Comprehensive statistics for all four BaatCheet splits: (a) BaatCheet_Syn_X2IL , (b) BaatCheet_Syn_IL2X , (c) BaatCheet_Human_Train , and (d) BaatCheet_Test .

	Open-Source LLMs					Dedicated MT Systems			Proprietary LLM
Lang Pair	Llama-3B	Llama-8B	Qwen3-4B	Gemma3-4B	Sarvam-T	GMT	IndicTrans2	BhashaVerse	GPT-4o-mini
(a) Reference-Based COMET
Eng $\rightarrow$ Hin	0.7282	0.7546	0.6989	0.7881	0.8038	0.7957	0.7524	0.7543	0.7857
Eng $\rightarrow$ Tam	0.5745	0.7141	0.4945	0.8109	0.7725	0.8623	0.8079	0.8100	0.8393
Eng $\rightarrow$ Tel	0.5964	0.6257	0.5023	0.7644	0.8056	0.8391	0.8296	0.8210	0.8255
Hin $\rightarrow$ Tam	0.5054	0.5675	0.5079	0.7998	0.6541	0.8791	0.8010	0.8554	0.8686

Appendix

Table 8: Language-pair disaggregated baseline evaluation across nine candidate systems on BaatCheet_Human_Train , evaluated using (a) reference-based COMET and (b) reference-free COMET-QE. Bold text indicates the highest score per language pair.

		Reference-Based COMET				Reference-Free COMET-QE
Lang Pair	Data Source	GMT	GPT	IndicTrans2	BhashaVerse	GMT	GPT	IndicTrans2	BhashaVerse
(a) Human_Train_X2IL ( $\text{X}\rightarrow\text{IL}$ Direction)
Eng $\rightarrow$ Hin	DailyTalk	0.7628	0.7506	0.7350	0.7257	0.8587	0.8474	0.8417	0.8297
	MuTual	0.8059	0.8064	0.7526	0.7724	0.8597	0.8492	0.8329	0.8440
	News-gen	0.8185	0.8000	0.7696	0.7649	0.8655	0.8527	0.8542	0.8544
	AVG	0.7957	0.7857	0.7524	0.7543	0.8613	0.8498	0.8430	0.8427

Appendix

Table 9: Candidate MT evaluation scores on BaatCheet_Human_Train candidate sets disaggregated by language pair and data source for both (a)

\text{X}\rightarrow\text{IL}

and (b)

\text{IL}\rightarrow\text{X}

directions (X are English and Hindi, IL are Hindi, Tamil and Telugu), evaluated using Reference-Based COMET and Reference-Free COMET-QE. Bold values indicate the highest score per row for each evaluation metric block.

	(a) Utt-Avg Strategy (Dialogue Counts, $N=510$ )				(b) Utt-High Strategy (Utterance Turn Counts, $N=8,427$ )
	X2IL Direction ( $\text{X}\rightarrow\text{IL}$ )		IL2X Direction ( $\text{IL}\rightarrow\text{X}$ )		X2IL Direction ( $\text{X}\rightarrow\text{IL}$ )		IL2X Direction ( $\text{IL}\rightarrow\text{X}$ )
MT Engine	COMET-QE	COMET	COMET-QE	COMET	COMET-QE	COMET	COMET-QE	COMET
GMT	320	320	199	79	3,506	3,449	3,210	2,811
GPT-4o-mini	154	127	294	431	2,690	2,541	3,720	4,725
IndicTrans2	32	37	16	0	1,154	1,186	1,017	573
BhashaVerse	4	26	1	0	1,077	1,251	480	318

Appendix

Table 10: Candidate MT system selection counts on BaatCheet_Human_Train candidate sets under both (a) Utt-Avg (dialogue-level engine selection across 510 dialogues) and (b) Utt-High (utterance-level turn selection across 8,427 turns) filtering strategies, evaluated using reference-free COMET-QE and reference-based COMET. Bold text highlights the most selected MT engine per column.

	(a) DOC-COMET Metric Validation				(b) COMTAIL Metric Validation
	Selection via COMET		Selection via COMET-QE		Selection via COMET		Selection via COMET-QE
Lang Pair	Utt-Avg	Utt-High	Utt-Avg	Utt-High	Utt-Avg	Utt-High	Utt-Avg	Utt-High
(a) Human_Train_X2IL( $\text{X}\rightarrow\text{IL}$ )
Eng $\rightarrow$ Hin	0.9049	0.9041	0.9057	0.9082	0.9177	0.9189	0.9213	0.9263
Eng $\rightarrow$ Tam	0.9012	0.9018	0.9033	0.9067	0.8198	0.8209	0.8270	0.8466
Eng $\rightarrow$ Tel	0.8932	0.8958	0.8958	0.9004	0.9277	0.9305	0.9320	0.9371

Appendix

Table 11: Selection strategy validation on BaatCheet_Human_Train candidate outputs comparing Utt-Avg vs Utt-High filtering strategies under both COMET and COMET-QE candidate selection, evaluated using (a) Context-aware DOC-COMET and (b) Indic-specialized COMTAIL metrics across all language directions. Bold text highlights the winning strategy under COMET-QE selection.

Figure 6: An example English $\rightarrow$ Telugu dialogue from BaatCheet_Human_Train illustrating the Utt-High COMET-QE selection strategy. Each utterance’s Telugu translation is independently selected from four candidate MT engines (GMT = Google Translate, GPT = GPT-4o-mini, BV = BhashaVerse, INT2 = IndicTrans2), with the corresponding COMET-QE score shown per turn. This shows that GMT dominance is higher, and the same is reflected in the utt-avg highest score; GMT is 0.869 in the same dialogue.

Translation Direction	Utt-Avg	Utt-High
X2IL Direction ( $\text{X}\rightarrow\text{IL}$ )	123 (55.9%)	90 (40.9%)
IL2X Direction ( $\text{IL}\rightarrow\text{X}$ )	128 (58.2%)	81 (36.8%)
Total	251 (57.0%)	171 (38.9%)

Appendix

Table 12: Human binary preference study results comparing Utt-Avg and Utt-High filtering strategies across 510 sampled dialogues.

Hyperparameter	Value
Number of epochs	1
Batch size	2
Gradient accumulation step	4
Learning rate	1e-4
Maximum sequence length	4096
LoRA rank	32

Appendix

Table 13: Training Hyperparameters.

Model	LP	Zero-Shot	Few-Shot	Syn_X2IL	Syn_IL2X	HT	Syn_X2IL+ HT	Syn_IL2X+ HT	Syn_Both +HT	Syn_Both +3*HT
Llama-3B	Eng-Hin	0.496	0.558	0.751	0.748	0.748	0.747	0.736	0.712	0.604
	Eng-Tam	0.539	0.572	0.775	0.699	0.672	0.733	0.670	0.769	0.727
	Eng-Tel	0.523	0.556	0.784	0.694	0.699	0.711	0.694	0.786	0.759
	Hin-Tam	0.567	0.642	0.790	0.753	0.723	0.764	0.730	0.778	0.786
	Hin-Tel	0.533	0.605	0.801	0.746	0.728	0.751	0.708	0.779	0.760
	AVGs	0.532	0.586	0.780	0.728	0.714	0.741	0.708	0.765	0.727

Appendix

Table 14: Utterance-level COMET scores on BaatCheet_Test. Shaded cells represent the best performance per language pair. Syn_Both denotes Syn_[X2IL+IL2X] and HT denotes Human_Train

Model	LP	Zero-Shot	Few-Shot	Syn_X2IL	Syn_IL2X	HT	Syn_X2IL+ HT	Syn_IL2X+ HT	Syn_Both +HT	Synt _Both +3*HT
Llama-3B	Eng-Hin	0.675	0.495	0.710	0.700	0.705	0.705	0.688	0.661	0.507
	Eng-Tam	0.556	0.552	0.682	0.577	0.587	0.675	0.584	0.688	0.659
	Eng-Tel	0.590	0.525	0.713	0.581	0.601	0.650	0.608	0.710	0.699
	Hin-Tam	0.499	0.531	0.555	0.602	0.588	0.590	0.593	0.617	0.729
	Hin-Tel	0.523	0.474	0.572	0.597	0.563	0.589	0.578	0.635	0.700
	AVGs	0.568	0.515	0.647	0.611	0.609	0.642	0.610	0.662	0.658

Appendix

Table 15: DOC-COMET on BaatCheet_Test. Shaded cells represent the best performance per language pair. Syn_Both denotes Syn_[X2IL+IL2X] and HT denotes Human_Train

Model	LP	Zero-Shot	Few-Shot	Syn_X2IL	Syn_IL2X	HT	Syn_X2IL+ HT	Syn_IL2X+ HT	Synt _Both +HT	Syn_Both +3*HT
Llama-3B	Eng-Hin	0.394	0.498	0.755	0.715	0.723	0.717	0.713	0.698	0.555
	Eng-Tam	0.417	0.434	0.615	0.524	0.513	0.558	0.506	0.610	0.553
	Eng-Tel	0.301	0.315	0.700	0.524	0.515	0.536	0.497	0.701	0.658
	Hin-Tam	0.390	0.430	0.621	0.549	0.529	0.567	0.535	0.609	0.604
	Hin-Tel	0.306	0.408	0.716	0.606	0.581	0.621	0.553	0.681	0.648
	AVGs	0.362	0.417	0.681	0.584	0.572	0.600	0.561	0.660	0.604

Appendix

Table 16: COMTAIL Scores on BaatCheet_Test. Shaded cells represent the best performance per language pair. Syn_Both denotes Syn_[X2IL+IL2X] and HT denotes Human_Train

Panel of Figure 7 — Figure 7: The prompt interface used for LLM-as-a-judge evaluation for Dialogue Level Evaluation. Text in brackets denotes dynamic variables.

Panel of Figure 8 — Figure 8: The prompt used for utterance-level evaluation. The dynamic variable {paired_utterances_text} contains the list of source and target sentence pairs.

Figure 9: Dialogue-Level Annotation Platform

Figure 10: Utterence-Level Annotation Platform

Figure 11: A sample dialogue in our error analysis suggests that there are still formal-style dialogues. While the baseline semantics are preserved, the translations are only somewhat natural. This finding supports our conclusion that synthetic data alone is insufficient for capturing deep conversational nuances, reinforcing the necessity for larger-scale, human-curated informal dialogue datasets.

7-Scale SQM Rubric	Eng–Hin	Eng–Tam	Eng–Tel	Hin–Tam	Hin–Tel
(a) Utterance-Level Accuracy
00–15: Completely unusable	0	0	0	0	0
16–30: Very poor translation	0	0	0	0	0
31–45: Meaning partially preserved	0	0	0	0	0
46–60: Meaning correct but unnatural	0	1	0	1	1
61–75: Mostly accurate and somewhat natural	6	25	23	13	11

Appendix

Table 17: SQM score distributions across language directions for Sarvam-Translate fine-tuned on BaatCheet_Human_Train , disaggregated by (a) Utterance-level Accuracy and Dialogue-level factors: (b) Naturalness, (c) Informality, and (d) Cohesion & Coherence. Bold values indicate peak distribution bands.

		GPT vs Human		Gemini vs Human		GPT vs Gemini
Factor	Model	$r$	$\tau$	$r$	$\tau$	$r$	$\tau$
Naturalness	Gemma3-4B	0.544	0.361	0.574	0.395	0.687	0.504
	Llama-3.2-3B	0.540	0.382	0.606	0.427	0.758	0.548
	Llama-3.1-8B	0.594	0.422	0.665	0.486	0.788	0.605
	Qwen3-4B	0.543	0.321	0.639	0.397	0.734	0.514
	Sarvam-T	0.539	0.367	0.639	0.435	0.723	0.529

Appendix

Table 18: Per-model inter-judge correlation (

r

= Pearson,

\tau

= Kendall’s

\tau

) across all four evaluation factors between GPT-4o-mini, Gemini-Flash-Lite, and Human Expert Linguists. Bold rows indicate cross-model averages.

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