The authors report scalable training of neural LMs from heterogeneous corpora for ASR second-pass rescoring, delivering 6.2% relative WER reduction with minimal latency increase.
How Transferable are Neural Networks in NLP Applications?
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abstract
Transfer learning is aimed to make use of valuable knowledge in a source domain to help model performance in a target domain. It is particularly important to neural networks, which are very likely to be overfitting. In some fields like image processing, many studies have shown the effectiveness of neural network-based transfer learning. For neural NLP, however, existing studies have only casually applied transfer learning, and conclusions are inconsistent. In this paper, we conduct systematic case studies and provide an illuminating picture on the transferability of neural networks in NLP.
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cs.CL 1years
2019 1verdicts
UNVERDICTED 1representative citing papers
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Scalable Multi Corpora Neural Language Models for ASR
The authors report scalable training of neural LMs from heterogeneous corpora for ASR second-pass rescoring, delivering 6.2% relative WER reduction with minimal latency increase.