Question generation by intent prediction

Patent metadata

Field	Value
Publication number	US-2022382993-A1
Application number	US-202117334714-A
Country	US
Kind code	A1
Filing date	May 29, 2021
Priority date	May 29, 2021
Publication date	Dec 1, 2022
Grant date	—

Abstract

Generating questions by receiving user utterance data, determining an intent confidence vector for the user utterance data, predicting, by a trained next user-intent prediction model, a next user-intent confidence vector using the intent confidence vector, and generating a next question using the next user-intent confidence vector.

First claim

What is claimed is:

1 . A computer system for question generation, the computer system comprising:
one or more computer processors; one or more computer readable storage devices; and stored program instructions on the one or more computer readable storage devices for execution by the one or more computer processors, the stored program instructions comprising:
program instructions to receive user utterance data;
program instructions to determine an intent confidence vector for the user utterance data;
program instructions to predict, by a trained next user-intent prediction model, a next user-intent confidence vector using the intent confidence vector; and
program instructions to generate a next question using the next user-intent confidence vector.

2 . The computer system according to claim 1 , the stored program instructions further comprising:
program instructions to predict a next user-intent confidence vector using a sequence of intent confidence vectors determined from a sequence of received user utterance data.

3 . The computer system according to claim 1 , the stored program instructions further comprising:
program instructions to train the next user-intent prediction model comprising: program instructions to receive historic chatbot conversation data, wherein the chatbot conversation data comprises labeled user-intents and intent confidence vectors for each user utterance of each conversation; program instructions to separate the chatbot conversation data into training data and validation data; program instructions to generate a matrix including all the intent confidence vectors for all historic conversation training data; program instructions to train a machine learning model to classify user-intents using the matrix of training data; program instructions to validate the machine learning model using the validation data; and program instructions to provide a trained next intent predictor mode for use in generating questions.

4 . The computer system according to claim 3 , the stored program instructions further comprising program instructions to drop out at least one intent confidence vector during training.

5 . The computer system according to claim 1 , the stored program instructions further comprising program instructions to train a long short term memory (LSTM) neural network with L1 regularization, as the next user-intent prediction model, and program instructions to provide the trained LSTM neural network or use predicting a next user-intent.

6 . The computer system according to claim 5 , the stored program instructions further comprising program instructions to train the LSTM neural network using a least absolute shrinkage and selection operator for regularization.

7 . A computer program product for question generation, the computer program product comprising one or more computer readable storage devices and collectively stored program instructions on the one or more computer readable storage devices, the stored program instructions comprising:
program instructions to receive user utterance data; program instructions to determine an intent confidence vector for the user utterance data; program instructions to predict, by a trained next user-intent prediction model, a next user-intent confidence vector using the intent confidence vector; and program instructions to generate a next question using the next user-intent confidence vector.

8 . The computer program product according to claim 7 , the stored program instructions further comprising:
program instructions to predict a next user-intent confidence vector using a sequence of intent confidence vectors determined from a sequence of received user utterance data.

9 . The computer program product according to claim 7 , the stored program instructions further comprising:
program instructions to train the next user-intent prediction model comprising: program instructions to receive historic chatbot conversation data, wherein the chatbot conversation data comprises labeled user-intents and intent confidence vectors for each user utterance of each conversation; program instructions to separate the chatbot conversation data into training data and validation data; program instructions to generate a matrix including all the intent confidence vectors for all historic conversation training data; program instructions to train a machine learning model to classify user-intents using the matrix of training data; program instructions to validate the trained machine learning model using the validation data; and program instructions to provide a trained next intent predictor mode for use in generating questions.

10 . The computer program product according to claim 9 , the stored program instructions further comprising program instructions to drop out at least one intent confidence vector during training.

11 . The computer program product according to claim 7 , the stored program instructions further comprising program instructions to train a long short term memory (LSTM) neural network with L1 regularization, as the next user-intent prediction model, and program instructions to provide the trained LSTM neural network or use predicting a next user-intent.

12 . The computer program product according to claim 11 , the stored program instructions further comprising program instructions to train the LSTM neural network using a least absolute shrinkage and selection operator for regularization.

13 . The computer program product according to claim 7 , wherein
program instructions to receive user utterance data comprises receiving data from a plurality of conversations.

14 . A computer implemented method for generating questions, the method comprising:
receiving, by one or more computer processors, user utterance data; determining, by the one or more computer processors, an intent confidence vector for the user utterance data; predicting, by the one or more computer processors through a trained next user-intent prediction model, a next user-intent confidence vector using the intent confidence vector; and generating, by the one or more computer processors, a next question using the next user-intent confidence vector.

15 . The computer implemented method according to claim 14 , further comprising:
predicting, by the one or more computer processors, a next user-intent confidence vector using a sequence of intent confidence vectors determined from a sequence of received user utterance data.

16 . The computer implemented method according to claim 14 , further comprising:
training, by the one or more computer processors, the next user-intent prediction model by: receiving, by the one or more computer processors, historic chatbot conversation data, wherein the chatbot conversation data comprises labeled user-intents and intent confidence vectors for each user utterance of each conversation; separating, by the one or more computer processors, the chatbot conversation data into training data and validation data; generating, by the one or more computer processors, a matrix including all the intent confidence vectors for all historic conversation training data; training, by the one or more computer processors, a machine learning model to classify user-intents using the matrix of training data; validating, by the one or more computer processors, the trained machine learning model using the validation data; and providing, by the one or more computer processors, a trained next intent predictor mode for use in generating questions.

17 . The computer implemented m

Assignees

• IBM

Inventors

• Lewis Jacob
• Asthana Ruchi
• Mallette Jennifer A
• Jones Steven Ware

Classifications

• G06F40/279

  Recognition of textual entities · CPC title

• G06F40/216

  using statistical methods · CPC title

• G06F40/35 Primary

  Discourse or dialogue representation · CPC title

• H04L51/02

  using automatic reactions or user delegation, e.g. automatic replies or chatbot-generated messages · CPC title

• G06F40/40

  Processing or translation of natural language (natural language analysis G06F40/20; semantic analysis G06F40/30) · CPC title