Transformer-Encoder Trees for Efficient Multilingual Machine Translation and Speech Translation

Abstract:Multilingual translation suffers from computational redundancy, especially when translating into multiple languages simultaneously. In addition, translation quality can suffer for low-resource languages. To address this, we introduce Transformer Encoder Tree (TET), a hierarchical, non-autoregressive encoder-only architecture trained with Connectionist Temporal Classification (CTC) for multilingual translation. TET shares intermediate representations among linguistically similar target languages, improving accuracy on low-resource languages while reducing computational redundancy and enabling the generation of all target languages in a single forward pass. TET eliminates the sequential bottleneck of autoregressive models and supports fully parallel decoding of all tokens across all target languages. Compared to a naive one-to-many multilingual design, TET reduces the total parameter count by 66% and lowers inference computation by 60%. In speech translation, combining TET with a non-autoregressive speech recognition backbone (Wav2Vec2) shows competitive translation quality compared to autoregressive systems while speeding up inference by approximately 7-14 times.

Computation and Language (cs.CL); Artificial Intelligence (cs.AI)

arXiv:2509.17930 [cs.CL]

(or arXiv:2509.17930v3 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2509.17930 Focus to learn more arXiv-issued DOI via DataCite

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