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Found 5 papers, 0 papers with code
Unsupervised Adaptation of Question Answering Systems via Generative Self-training
Nevertheless, additional pre-training closer to the end-task, such as training on synthetic QA pairs, has been shown to improve performance.
CNNBiF: CNN-based Bigram Features for Named Entity Recognition
More importantly our fine-tuned CoNLL2003 model displays significant gains in generalization to out of domain datasets: on the OntoNotes subset we achieve an F1 of 72. 67 which is 0. 49 points absolute better than the baseline, and on the WNUT16 set an F1 of 68. 22 which is a gain of 0. 48 points.
Are the Tools up to the Task? an Evaluation of Commercial Dialog Tools in Developing Conversational Enterprise-grade Dialog Systems
There has been a significant investment in dialog systems (tools and runtime) for building conversational systems by major companies including Google, IBM, Microsoft, and Amazon.
Quantized-Dialog Language Model for Goal-Oriented Conversational Systems
The key idea is to quantize the dialog space into clusters and create a language model across the clusters, thus allowing for an accurate choice of the next utterance in the conversation.
Direct Acoustics-to-Word Models for English Conversational Speech Recognition
Our CTC word model achieves a word error rate of 13. 0%/18. 8% on the Hub5-2000 Switchboard/CallHome test sets without any LM or decoder compared with 9. 6%/16. 0% for phone-based CTC with a 4-gram LM.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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