Dialogue behavior recognition method based on deep neural network and conditional random field

Abstract

The invention discloses a dialogue act identification method based on deep neural networks and conditional random fields. The dialogue act identification method comprises the steps of: 1, pre-treating a data-intensive spoken Chinese corpus to extract multimodal features; 2, constructing a conditional random field model; 3, constructing a state feature function of the conditional random field model based on the multimodal deep neural networks; 4, maximizing a log-likelihood function to solve parameters of the conditional random field model; and 5, obtaining the dialogue act corresponding to each sentence in the dialogue by solving the dialogue act sequence of the whole dialogue. According to the dialogue act identification method, abstract features more relevant to a classification task can be learned from the original features, more efficient fusion of the multimodal information in the dialogue acts can be achieved so as to establish a good foundation for subsequent classification tasks, meanwhile the sequence dependency relationship of label samples can be better depicted, and the conditional random fields are proposed as main body frames, and thereby each dialogue is integrally optimized.

Description

technical field [0001] Based on machine learning and statistical learning theory, the present invention combines the multimodal deep neural network with the conditional random field by using the multimodal deep neural network to learn the characteristic functions in the conditional random field to form an efficient and applicable A model for solving complex sequence learning problems, and finally the model is used for Chinese dialogue act recognition. Background technique [0002] Dialogue acts (Dialogue acts, DAs) were put forward by Austin in 1996 based on speech acts. To a certain extent, they reflect the speaker's intention and are of great significance for determining the pragmatic information of sentences. Dialogue action recognition is a key step for computers to understand natural language, and it plays an important role in many application fields such as human-computer dialogue, interactive information retrieval, machine translation, and interactive question-answeri...

AI Technical Summary

Problems solved by technology

[0007] 4) Intention recognition is a very complicated problem due to the complex dependencies between the extracted features and the dialogue behavior markers, even a system as complex as the human brain is difficult to recognize intents

However, it is difficult for classical deep learning models to describe the sequence dependencies between pairs of markers

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