RNN based labeled data generation for spoken language understanding
In spoken language understanding, getting manually labeled data such as domain, intent and slot labels is usually required for training classifiers. Starting with some manually labeled data, we propose a data generation approach to augment the training set with synthetic data sampled from a joint distribution between an input query and an output label. We propose using a recurrent neural network to model the joint distribution and sample synthetic data for classifier training. Evaluated on ATIS and live logs of Cortana, a Microsoft voice personal assistant, we showed consistent performance improvement on domain classification, intent classification, and slot tagging on multiple languages.
INTERSPEECH