DIALOGUE ACT ESTIMATION METHOD, DIALOGUE ACT ESTIMATION APPARATUS, AND STORAGE MEDIUM

• US 20170372694A1
• Filed: 06/07/2017
• Published: 12/28/2017
• Est. Priority Date: 06/23/2016
• Status: Active Grant

• Current Assignee: Panasonic Intellectual Property Management Co., Ltd. (Panasonic Holdings Corporation)
• Original Assignee: Panasonic Intellectual Property Management Co., Ltd. (Panasonic Holdings Corporation)
• Inventors: USHIO, TAKASHI
• Granted Patent: US 10,460,721 B2
• Time in Patent Office: Days
• CPC Class Codes: G06F 16/353 into predefined classes
• CPC Class Codes: G06F 40/35 Discourse or dialogue representation
• CPC Class Codes: G06N 3/044 Recurrent networks, e.g. Hopfield networks
• CPC Class Codes: G06N 3/084 Backpropagation, e.g. using gradient descent
• CPC Class Codes: G10L 15/063 Training
• CPC Class Codes: G10L 15/16 using artificial neural networks
• CPC Class Codes: G10L 15/1815 Semantic context, e.g. disambiguation of the recognition hypotheses based on word meaning
• CPC Class Codes: G10L 15/22 Procedures used during a speech recognition process, e.g. man-machine dialogue
• CPC Class Codes: G10L 17/22 Interactive procedures; Man-machine interfaces
• CPC Class Codes: G10L 21/0272 Voice signal separating

Abstract

A dialogue act estimation method, in a dialogue act estimation apparatus, includes acquiring first training data indicating, in a mutually associated manner, text data of a first sentence that can be a current uttered sentence, text data of a second sentence that can be an uttered sentence immediately previous to the first sentence, speaker change information indicating whether a speaker of the first sentence is the same as a speaker of the second sentence, and dialogue act information indicating a class of the first sentence, learning an association between the current uttered sentence and the dialogue act information by applying the first training data to a model, and storing a result of the learning as learning result information in a memory.

Claims

1. A dialogue act estimation method, in a dialogue act estimation apparatus, comprising:

acquiring first training data indicating, in a mutually associated manner, text data of a first sentence that can be a current uttered sentence, text data of a second sentence that can be an uttered sentence immediately previous to the first sentence, first speaker change information indicating whether a speaker of the first sentence is the same as a speaker of the second sentence, and dialogue act information indicating a class of the first sentence;

learning an association between the current uttered sentence and the dialogue act information by applying the first training data to a model; and

storing a result of the learning as learning result information in a memory.

2. The dialogue act estimation method according to claim 1, wherein the first training data further includes first speaker identification information indicating the speaker of the first sentence and second speaker identification information indicating the speaker of the second sentence.

3. The dialogue act estimation method according to claim 2, wherein the model includes

a first model that outputs a first feature vector based on the text data of the first sentence, the text data of the second sentence, the first speaker identification information, the second speaker identification information, and a first weight parameter,

a second model that outputs a second feature vector based on the text data of the first sentence, the text data of the second sentence, the first speaker change information, and a second weight parameter, and

a third model that outputs a posterior probability of a dialogue act corresponding to the first sentence based on the first feature vector, the second feature vector, and a third weight parameter,

wherein the learning is performed by performing error backpropagation between the posterior probability and the dialogue act information indicated by the first training data.

4. The dialogue act estimation method according to claim 3, wherein

the first model determines the first feature vector from the first sentence and the second sentence according to a first RNN-LSTM (Recurrent Neural Network-Long Short Term Memory) having the first weight parameter dependent on the first speaker identification information and the second speaker identification information, and

the second model determines the second feature vector from the first sentence and the second sentence according to a second RNN-LSTM having the second weight parameter dependent on first speaker change information.

5. The dialogue act estimation method according to claim 1, wherein the first training data is acquired from a corpus in which two or more pieces of training data are accumulated.

6. The dialogue act estimation method according to claim 1, further comprising

acquiring dialogue data including text data of a third sentence of a current uttered sentence uttered by a user, text data of a fourth sentence of an uttered sentence immediately previous to the third sentence, and second speaker change information indicating whether the speaker of the third sentence is the same as a speaker of the fourth sentence, and

estimating a dialogue act to which the third sentence is classified by applying the dialogue data to the model based on the learning result information.

7. The dialogue act estimation method according to claim 1, wherein at least one of the acquiring, the learning, and the storing is performed by a processor.

8. A dialogue act estimation apparatus comprising:

a training data acquirer that acquires first training data indicating, in a mutually associated manner, text data of a first sentence that can be a current uttered sentence, text data of a second sentence that can be an uttered sentence immediately previous to the first sentence, first speaker change information indicating whether a speaker of the first sentence is the same as a speaker of the second sentence, and dialogue act information indicating a class of the first sentence;

a learner that learns an association between the current uttered sentence and the dialogue act information by applying the first training data to a model; and

a storage that stores a result of the learning as learning result information in a memory.

9. The dialogue act estimation apparatus according to claim 8, wherein at least one of the acquisition unit, the learner, and the storage includes a processor.

10. A non-transitory storage medium storing a computer-readable program, the program causing a computer to execute a process including:

acquiring first training data indicating, in a mutually associated manner, text data of a first sentence that can be a current uttered sentence, text data of a second sentence that can be an uttered sentence immediately previous to the first sentence, first speaker change information indicating whether a speaker of the first sentence is the same as a speaker of the second sentence, and dialogue act information indicating a class of the first sentence;

learning an association between the current uttered sentence and the dialogue act information by applying the first training data to a model; and

storing a result of the learning as learning result information in a memory.