Microsoft Technology Licensing, LLC

Yun-Nung Vivian Chen

Li Deng

Jianfeng Gao

Xiujun Li

Dilek Zeynep Hakkani-Tur

Gokhan Tur

Paul Anthony Crook

Lihong Li

Asli Celikyilmaz

Bhuwan Dhingra

Faisal Ahmed

Xuesong Yang

Systems and methods for determining knowledge-guided information for a recurrent neural networks (RNN) to guide the RNN in semantic tagging of an input phrase are presented. A knowledge encoding module of a Knowledge-Guided Structural Attention Process (K-SAP) receives an input phrase and, in conjunction with additional sub-components or cooperative components generates a knowledge-guided vector that is provided with the input phrase to the RNN for linguistic semantic tagging. Generating the knowledge-guided vector comprises at least parsing the input phrase and generating a corresponding hierarchical linguistic structure comprising one or more discrete sub-structures. The sub-structures may be encoded into vectors along with attention weighting identifying those sub-structures that have greater importance in determining the semantic meaning of the input phrase.

Knowledge-guided structural attention processing

Described herein are systems, methods, and techniques by which a processing unit can build an end-to-end dialogue agent model for end-to-end learning of dialogue agents for information access and apply the end-to-end dialogue agent model with soft attention over knowledge base entries to make the dialogue system differentiable. In various examples the processing unit can apply the end-to-end dialogue agent model to a source of input, fill slots for output from the knowledge base entries, induce a posterior distribution over the entities in a knowledge base or induce a posterior distribution of a target of the requesting user over entities from a knowledge base, develop an end-to-end differentiable model of a dialogue agent, use supervised and/or imitation learning to initialize network parameters, calculate a modified version of an episodic algorithm. e.g., the REINFORCE algorithm, for training an end-to-end differentiable model based on user feedback.

End-to-end learning of dialogue agents for information access

A processing unit can operate an end-to-end recurrent neural network (RNN) with limited contextual dialog memory that can be jointly trained by supervised signals-user slot tagging, intent prediction and/or system action prediction. The end-to-end RNN, or joint model has shown advantages over separate models for natural language understanding (NLU) and dialog management and can capture expressive feature representations beyond conventional aggregation of slot tags and intents, to mitigate effects of noisy output from NLU. The joint model can apply a supervised signal from system actions to refine the NLU model. By back-propagating errors associated with system action prediction to the NLU model, the joint model can use machine learning to predict user intent by a binary classification obtained by both forward and backward output, and perform slot tagging, and make system action predictions based on user input, e.g., utterances across a number of domains.

Joint language understanding and dialogue management using binary classification based on forward and backward recurrent neural network

Growing community of inventors

Taipei, Taiwan

Yun-Nung Vivian Chen