Conversation Classification Model Domain Adaptation via Weighted Training
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Solution Overview
Problem
Classifiers trained for specific domains, such as financial conversations, fail to accurately classify conversations from different domains without adequate training data, leading to inefficiencies in predicting outcomes and taking necessary organizational steps.
Innovation Solution
A method and system for training a conversation classification model by receiving conversations from a source and target domain, generating transcripts through speech-to-text conversion, extracting features, assigning weights based on similarity, and training the model to assign predetermined tags to target domain conversations, enabling domain adaptation without requiring labeled data from the target domain.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a classifier is trained using training data from a specific domain (e.g., financial conversations), then the classifier achieves high accuracy for that domain, but it fails to accurately classify conversations from other domains
Solution Approach 1:
The patent changes the parameters of the training process by introducing domain similarity weights that dynamically adjust the contribution of source domain samples based on their relevance to the target domain. This allows the classifier to adapt to different domains while maintaining high accuracy through weighted training rather than fixed domain-specific training
2Measurement precision
If a classifier is trained on target domain data to improve accuracy for that domain, then classification performance improves, but the cost and complexity of data collection and labeling increase
Solution Approach 1:
The patent introduces an intermediary mechanism - domain similarity calculation based on feature extraction - that bridges the source domain and target domain. This intermediary allows the system to leverage source domain data without requiring extensive target domain labeled data, reducing the complexity of data collection and labeling while maintaining accuracy
Solution Approach 2:
The patent performs preliminary feature extraction and domain similarity assessment before the actual classification training. By pre-processing and analyzing the characteristics of both source and target domains beforehand, the system prepares weighted training data that reduces the need for extensive target domain labeling, thereby lowering the complexity of the overall training process
Data Source
AI summary
Methods and systems for training a conversation-classification model are disclosed. A first set of conversations in a source domain and a second set of conversation in a target domain are received. Each of the first set of conversations has an associated predetermined tag. One or more features are extracted from the first set of conversations and from the second set of conversations. Based on the similarity of content in the first set of conversations and the second set of conversations, a first weight is assigned to each conversation of the first set of conversations. Further, a second weight is assigned to the one or more features of the first set of conversations based on the similarity of the one or more features of the first set of conversations and of the second set of conversations. A conversation-classification model is trained based on the first weight and the second weight.