Conversational Domain Adaptation of IndicTrans2 across 21 Indic Languages via Experience Replay and Model Soups
Researchers have solved a persistent problem in machine translation: adapting models to conversational speech without sacrificing general-domain performance. By combining experience replay (mixing general training data back in) with model souping (averaging weights across fine-tuned and base models), the team improved IndicTrans2's conversational quality across all 21 Indic languages by an average of 6.2 chrF points while holding general performance flat. This technique addresses a real deployment friction point for translation systems and demonstrates how simple ensemble methods can eliminate the usual accuracy-versus-domain-specificity tradeoff, with implications for any multilingual system facing similar register adaptation challenges.
Modelwire context
ExplainerThe paper's actual contribution is methodological economy: it shows that you don't need architectural changes or complex regularization to handle register adaptation in multilingual systems. The technique is deliberately simple, which is why it generalizes across 21 languages without tuning.
This connects to the clinical LLM paper from the same day, which found that models internally represent information they don't surface in outputs. Here, the inverse problem appears: IndicTrans2 already contains conversational competence in its weights, but fine-tuning on speech data erases general-domain knowledge. Model souping recovers that buried capacity by averaging rather than replacing. Both papers suggest that model internals often contain more than their behavior reveals, and that ensemble methods (averaging, probing) can recover what single-path inference discards.
If the same experience replay plus souping approach produces comparable gains on a held-out Indic language pair not in the original 21 (or on a non-Indic multilingual system like mBART), that confirms the method is genuinely transfer-agnostic. If gains collapse on a new language, it suggests the technique is overfitted to IndicTrans2's architecture or the specific language families tested.
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MentionsIndicTrans2 · OpenSubtitles · FLORES · Tatoeba · BPCC-H-Daily
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