Generating Training Data For A Conversational Query Response System

Patent metadata

Field	Value
Publication number	US-2018032902-A1
Application number	US-201615221483-A
Country	US
Kind code	A1
Filing date	Jul 27, 2016
Priority date	Jul 27, 2016
Publication date	Feb 1, 2018
Grant date	—

Abstract

Training tuples including text and a question and answer corresponding to the text are input to a machine learning algorithm, such as a deep neural network. A Q&A model is obtained that outputs questions and answers given an input text. The training tuples may be obtained from standardized test such that the text is a question prompt and the questions and answers are based on the prompt. Raw text is input to the Q&A model to obtain second training tuples including a question and an answer. An NLU model is trained according to the second training tuples. The NLU model may then be installed on a consumer device, which will then use the model to respond to conversational queries and provide an appropriate response.

First claim

What is claimed is:

1 . A method for training a query-response model for use in a vehicle, the method comprising, by a computer system:
training a first model using a first plurality of tuples each including text, a question, and an answer; processing unstructured data using the first model to obtain a second plurality of tuples each including a question and an answer; and training a second model using the second plurality of tuples.

2 . The method of claim 1 , further comprising loading the second model onto a consumer computing device.

3 . The method of claim 2 , wherein the consumer computing device is an in-vehicle infotainment (IVI) system mounted in a vehicle.

4 . The method of claim 3 , further comprising:
programming the IVI system to receive a query, input the query to the second model, and output a response according to the second model.

5 . The method of claim 3 , further comprising:
programming the IVI system to input voice queries to the second model and output a response to the query according to the second model.

6 . The method of claim 1 , wherein the first model is a deep neural network (DNN) model.

7 . The method of claim 1 , wherein the second model is a deep neural network (DNN) model.

8 . The method of claim 1 , wherein processing the unstructured data using the first model comprises:
pre-processing, by the computer system, the unstructured data to identify a feature set from within the unstructured data; and inputting, by the computer system, the feature set to the first model.

9 . The method of claim 1 , wherein the unstructured data comprises at least one of text and images.

10 . The method of claim 1 , wherein the first plurality of tuples are derived from test preparation materials for students.

11 . A system for training a query-response model comprising:
a first machine learning module including at least one processing device, the machine learning module programmed to:
train a first model using a first plurality of tuples each including text, a question, and an answer;
process unstructured data using the first model to obtain a second plurality of tuples each including a question and an answer; and
a second machine learning module programmed to train a second model using the second plurality of tuples, the second model being a natural language understanding (NLU) model.

12 . The system of claim 11 , wherein the second machine learning module is further programmed to cause the one or more processors to load the second model onto a consumer computing device.

13 . The system of claim 12 , wherein the consumer computing device is an in-vehicle infotainment (IVI) system mounted in a vehicle.

14 . The system of claim 13 , wherein the second machine learning module is further programmed to program the IVI system to receive a query, input the query to the second model, and output a response according to the second model.

15 . The system of claim 13 wherein the second machine learning module is further programmed to program the IVI system, to input voice queries to the second model and output a response to the query according to the second model.

16 . The system of claim 11 , wherein the first model is a deep neural network (DNN) model.

17 . The system of claim 11 , wherein the second model is a deep neural network (DNN) model.

18 . The system of claim 11 , wherein the first machine learning module is further programmed to process the unstructured data using the first model by:
pre-processing the unstructured data to identify a feature set from within the unstructured data; and inputting the feature set to the first model.

19 . The system of claim 11 , wherein the unstructured data comprises at least one of text and images.

20 . The system of claim 11 , wherein the first machine learning module is further programmed to derive the first plurality of tuples from test preparation materials for students.

Assignees

• Ford Global Tech Llc

Inventors

• Krishnan Lakshmi
• Han Kyu Jeong
• Charette Francois
• Puskorius Gintaras Vincent

Classifications

• G06N3/045

  Combinations of networks · CPC title

• G06N3/0895

  Weakly supervised learning, e.g. semi-supervised or self-supervised learning · CPC title

• G06N3/09

  Supervised learning · CPC title

• G06F17/30684

  Physics · mapped topic

• G06N99/005 Primary

  Physics · mapped topic