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Found 6 papers, 0 papers with code
Contextual Domain Classification with Temporal Representations
In commercial dialogue systems, the Spoken Language Understanding (SLU) component tends to have numerous domains thus context is needed to help resolve ambiguities.
Multi-task Learning of Spoken Language Understanding by Integrating N-Best Hypotheses with Hierarchical Attention
The comparison shows that our model could recover the transcription by integrating the fragmented information among hypotheses and identifying the frequent error patterns of the ASR module, and even rewrite the query for a better understanding, which reveals the characteristic of multi-task learning of broadcasting knowledge.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+6
Enhance Robustness of Sequence Labelling with Masked Adversarial Training
Adversarial training (AT) has shown strong regularization effects on deep learning algorithms by introducing small input perturbations to improve model robustness.
Ranked #3 on
Chunking
on CoNLL 2000
(using extra training data)
SeqVAT: Virtual Adversarial Training for Semi-Supervised Sequence Labeling
Virtual adversarial training (VAT) is a powerful technique to improve model robustness in both supervised and semi-supervised settings.
Ranked #7 on
Chunking
on CoNLL 2000
Improving Spoken Language Understanding By Exploiting ASR N-best Hypotheses
The NLU module usually uses the first best interpretation of a given speech in downstream tasks such as domain and intent classification.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
Ensemble Multi-task Gaussian Process Regression with Multiple Latent Processes
Multi-task/Multi-output learning seeks to exploit correlation among tasks to enhance performance over learning or solving each task independently.
