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Oracle analysis shows that substantially larger WER reductions are possible through utterance-level scale selection and identifies how beneficial adjustments vary with domain shifts.
This work explores how diffusion can enable multilingual translation with a discrete diffusion framework that refines all target languages in parallel, achieving sublinear latency scaling with the number of targets, and supports deployment as a single unified model to replace multiple independent systems.
Transformer Encoder Tree is introduced, a hierarchical, non-autoregressive encoder-only architecture trained with Connectionist Temporal Classification for multilingual translation that eliminates the sequential bottleneck of autoregressive models and supports fully parallel decoding of all tokens across all target lan...
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