Method for using a multi-scale recurrent neural network with pretraining for spoken language understanding tasks
View Patent ↗A spoken language understanding (SLU) system receives a sequence of words corresponding to one or more spoken utterances of a user, which is passed through a spoken language understanding module to produce a sequence of intentions. The sequence of words are passed through a first subnetwork of a multi-scale recurrent neural network (MSRNN), and the sequence of intentions are passed through a second subnetwork of the multi-scale recurrent neural network (MSRNN). Then, the outputs of the first subnetwork and the second subnetwork are combined to predict a goal of the user.
1. A spoken language understanding (SLU) method, comprising steps of:
receiving a sequence of words corresponding to one or more spoken utterances of a user;
passing the sequence of words through a spoken language understanding module to produce a sequence of intentions;
passing the sequence of words through a first subnetwork of a multi-scale recurrent neural network (MSRNN);
passing the sequence of intentions through a second subnetwork of the multi-scale recurrent neural network (MSRNN);
combining outputs of the first subnetwork and the second subnetwork to predict a goal of the user, wherein the steps are performed in a processor.
2. The method of claim 1 , wherein the sequence of words is an output of an automatic speech recognitions (ASR) system.
3. The method of claim 2 , wherein the sequence of words is a probability distribution over a set of words corresponding to the one or more spoken utterances of the user.
4. The method of claim 1 , wherein the goal is input to a dialog manager to output an action to be performed by a spoken language dialog system.
5. The method of claim 1 , wherein each intention in the sequence of intentions is a probability distribution over a set of intentions that correspond to the one or more spoken utterance of the user.
6. The method of claim 1 wherein the network parameters for the multi-scale recurrent neural network (MSRNN) are trained jointly using separate pre-trained initialization parameters for the first subnetwork and the second subnetwork.