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This paper addresses the task of contextual translation using multi-segmentmodels. Specifically we show that increasing model capacity further pushes thelimits of this approach and that deeper models are more suited to capturecontext dependencies. Furthermore, improvements observed with larger models canbe transferred to smaller models using knowledge distillation. Our experimentsshow that this approach achieves competitive performance across severallanguages and benchmarks, without additional language-specific tuning and taskspecific architectures.

A baseline revisited: Pushing the limits of multi-segment models for context-aware translation