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

• URL: http://arxiv.org/abs/2509.17930v1
• Date: Mon, 22 Sep 2025 15:52:18 GMT
• Title: Transformer-Encoder Trees for Efficient Multilingual Machine Translation and Speech Translation
• Authors: Yiwen Guan, Jacob Whitehill,
• Abstract summary: We propose a novel hierarchical Transformer Tree (TET) combined with non-autoregressive encoder-only models trained with Connectionist Temporal Classification for multilingual translation.<n>For speech translation, combining TET with a non-autoregressive speech recognition backbone (wav2vec2) shows promising results in terms of translation quality compared to autoregressive systems while being 7-14 times faster.
• Score: 2.7023796303812193
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multilingual translation faces challenges of computational redundancy and limited accuracy for low-resource languages, especially in speech translation. To address this, we propose a novel hierarchical Transformer Encoder Tree (TET) combined with non-autoregressive encoder-only models trained with Connectionist Temporal Classification for multilingual translation. By sharing intermediate representations among linguistically similar target languages, TET can improve accuracy on low-resource languages, reduce computational redundancy, and allow generating all target languages in a single forward pass, thus eliminating sequential bottlenecks and improving parallelism. For speech translation, combining TET with a non-autoregressive speech recognition backbone (wav2vec2) shows promising results in terms of translation quality compared to autoregressive systems while being 7-14 times faster.

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