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EELBERT: Tiny Models through Dynamic Embeddings
We introduce EELBERT, an approach for compression of transformer-based models (e. g., BERT), with minimal impact on the accuracy of downstream tasks.
Complementary Language Model and Parallel Bi-LRNN for False Trigger Mitigation
In this paper, we propose a novel solution to the FTM problem by introducing a parallel ASR decoding process with a special language model trained from "out-of-domain" data sources.
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