Pre-trained encoder-decoder transformers fine-tuned for sequence-to-sequence constituent parsing outperform prior seq2seq models and compete with specialized parsers on continuous treebanks.
Improving cross-lingual transfer learn- ing for end-to-end speech recognition with speech translation
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MCAT scales MLLMs to many-to-many speech translation across 70 languages via curriculum learning and a 30-token speech adapter, surpassing prior SOTA on FLEURS while improving speed.
ESRT achieves SOTA many-to-many S2TT across 45 languages on FLEURS via edge-cloud split inference that compresses features 10x and a multi-task curriculum learning strategy for cross-lingual balance.
citing papers explorer
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Exploiting Pre-trained Encoder-Decoder Transformers for Sequence-to-Sequence Constituent Parsing
Pre-trained encoder-decoder transformers fine-tuned for sequence-to-sequence constituent parsing outperform prior seq2seq models and compete with specialized parsers on continuous treebanks.
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Bandwidth-Efficient and Privacy-Preserving Edge-Cloud Many-to-Many Speech Translation
ESRT achieves SOTA many-to-many S2TT across 45 languages on FLEURS via edge-cloud split inference that compresses features 10x and a multi-task curriculum learning strategy for cross-lingual balance.