Multi-Task Neural Network for Dialogue Act Estimation
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Solution Overview
Problem
Existing dialogue act estimation techniques face challenges in achieving high accuracy due to insufficient training data and failure to utilize important context information from successive utterances, leading to inadequate estimation accuracy.
Innovation Solution
A method that acquires and processes learning data including multiple types of information (act, property, and dialogue act) associated with uttered sentences, using multi-task learning with neural networks to enhance the association between sentences and their respective information, incorporating speaker identification to improve context dependence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a recurrent neural network is used to learn dialogue act dependent on context, then context information is utilized, but training data is insufficient and accuracy is not high enough
Solution Approach 1:
The patent combines three separate learning tasks (dialogue act classification, property classification, and speaker identification) into a single multi-task learning framework. By merging these tasks and sharing neural network layers, the system effectively increases the utilization of limited training data across multiple objectives, improving dialogue act estimation accuracy despite data constraints
Solution Approach 2:
The neural network model is designed to perform multiple functions simultaneously: classifying dialogue acts, identifying properties, and recognizing speakers. This multi-functional approach allows the same training data to serve multiple purposes, effectively increasing the information extracted from limited training examples and improving overall estimation accuracy
2Measurement precision
If only single-task learning is performed for dialogue act classification, then the model is simpler, but important context information from successive utterances is not utilized
Solution Approach 1:
The patent merges dialogue act classification, property classification, and speaker identification into a unified multi-task learning model. This integration allows the system to leverage context information from successive utterances across multiple tasks, improving dialogue act estimation accuracy while maintaining manageable complexity through shared neural network components
Solution Approach 2:
The model is segmented into distinct task-specific output layers while sharing common feature extraction layers. This segmentation allows each task to benefit from context information independently while sharing computational resources, balancing model complexity with improved accuracy through contextual understanding
Data Source
AI summary
A dialog act estimation method includes acquiring learning data including a first sentence to be estimated in the form of text data of a first uttered sentence uttered at a first time point, a second sentence which is text data of a second uttered sentence uttered, at a time point before the first time point, successively after the first uttered sentence, act information indicating an act associated to the first sentence, property information indicating a property information associated to the first sentence, and dialog act information indicating a dialog act in the form of a combination of an act and a property associated to the first sentence, making a particular model learn three or more tasks at the same time using the learning data, and storing a result of the learning as learning result information in a memory.