Dialog Relationship Prediction Using Speaker and Trigger Word Cues
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Solution Overview
Problem
The existing methods for processing conversational data face challenges in accurately predicting relationships between statements or speakers due to the complexity in extracting semantic features, leading to reduced prediction accuracy.
Innovation Solution
A method is introduced that includes performing semantic feature extraction, relationship prediction, masked speaker prediction, and trigger word prediction, with additional losses to train an initial relationship prediction model, incorporating an initial phrase prediction network and trigger word prediction network to enhance the model's learning of speaker and trigger word features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If semantic feature extraction is performed on dialog text and statement or speaker pairs using an initial relationship prediction model, then relationship prediction can be performed, but the prediction accuracy is reduced due to the complexity of semantic feature extraction
Solution Approach 1:
The patent segments the relationship prediction task into multiple independent prediction components: speaker prediction, trigger word prediction, and relationship prediction. Each component is processed separately with its own prediction head and loss function, allowing the complex semantic feature extraction to be divided into manageable segments that can be optimized independently.
Solution Approach 2:
The patent adds new prediction dimensions beyond traditional relationship prediction. By introducing speaker prediction (predicting which speaker said what) and trigger word prediction (predicting key words that indicate relationships), the model extracts information from additional dimensional perspectives, enriching the semantic feature representation and improving relationship prediction accuracy.
2Measurement precision
If masked speaker prediction and trigger word prediction are added to the model, then more effective information is provided for relationship prediction, but the model complexity increases
Solution Approach 1:
The patent implements a multi-functional prediction model where a single model structure performs multiple prediction tasks simultaneously: relationship prediction, speaker prediction, and trigger word prediction. All three prediction heads share the same underlying semantic feature extraction layers, allowing the model to serve multiple functions while avoiding redundant computation and maintaining efficiency.
Solution Approach 2:
The patent incorporates feedback mechanisms through multiple loss functions that guide the model training. The speaker prediction loss and trigger word prediction loss provide feedback signals that help the model learn better semantic representations, which in turn improve the relationship prediction accuracy. This feedback loop allows the model to iteratively refine its predictions across all three tasks.
Data Source
AI summary
A method for processing a dialog relationship includes performing semantic feature extraction on a sample dialog text and sample statement or speaker pairs by an initial relationship prediction model, and performing relationship prediction based on the sample text semantic information and an actual statement or speaker relationship to determine a first loss based on a relationship prediction result. The method further includes performing masked speaker prediction based on the sample text semantic information to determine a second loss based on a masked speaker prediction result. The masked speaker prediction result represents a prediction of speakers masked in the sample dialog text. The method further includes performing trigger word prediction based on the sample text semantic information to determine a third loss, and training the initial relationship prediction model based on the first loss, the second loss and the third loss to obtain a dialog relationship prediction model.