Gated fusion of fastText and BERT embeddings into an end-to-end ASR model captures multi-sentence conversational context and lowers word error rate on the Switchboard corpus.
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cs.CL 2years
2019 2verdicts
UNVERDICTED 2representative citing papers
A label consistency training framework improves F1 on the ProPara benchmark for procedural text comprehension by using multiple independent descriptions of the same process.
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Gated Embeddings in End-to-End Speech Recognition for Conversational-Context Fusion
Gated fusion of fastText and BERT embeddings into an end-to-end ASR model captures multi-sentence conversational context and lowers word error rate on the Switchboard corpus.
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Be Consistent! Improving Procedural Text Comprehension using Label Consistency
A label consistency training framework improves F1 on the ProPara benchmark for procedural text comprehension by using multiple independent descriptions of the same process.