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arXiv · 2508.19856

TokenVerse++: Towards Flexible Multitask Learning with Dynamic Task Activation

Shashi Kumar·Srikanth Madikeri·Esaú Villatoro-Tello·Sergio Burdisso·Pradeep Rangappa·Andrés Carofilis·Petr Motlicek·Karthik Pandia·Shankar Venkatesan·Kadri Hacioğlu·Andreas Stolcke
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Abstract

Token-based multitasking frameworks like TokenVerse require all training utterances to have labels for all tasks, hindering their ability to leverage partially annotated datasets and scale effectively. We propose TokenVerse++, which introduces learnable vectors in the acoustic embedding space of the XLSR-Transducer ASR model for dynamic task activation. This core mechanism enables training with utterances labeled for only a subset of tasks, a key advantage over TokenVerse. We demonstrate this by successfully integrating a dataset with partial labels, specifically for ASR and an additional task, language identification, improving overall performance. TokenVerse++ achieves results on par with or exceeding TokenVerse across multiple tasks, establishing it as a more practical multitask alternative without sacrificing ASR performance.

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Shashi Kumar, Srikanth Madikeri, Esaú Villatoro-Tello, Sergio Burdisso, Pradeep Rangappa, Andrés Carofilis, Petr Motlicek, Karthik Pandia, Shankar Venkatesan, Kadri Hacioğlu, Andreas Stolcke. 2025-08-27. TokenVerse++: Towards Flexible Multitask Learning with Dynamic Task Activation. https://arxiv.org/abs/2508.19856

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