Conversational Agent Response Template Clustering
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Solution Overview
Problem
Conversational agents using neural conversation models face limitations in providing consistent and comprehensive responses to user queries, as they often generate short and simple answers, and are difficult to implement as practical user interfaces due to inconsistencies and the need to enumerate all possible user queries in advance.
Innovation Solution
A method and system for generating training data for conversational agents by clustering historical text dialogues, generating response templates based on agent messages within each cluster, and assigning these templates to messages using a similarity metric, allowing for the creation of high-quality training data that enables a predictive model to efficiently respond to user queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a neural conversation model is used to generate replies, then the usefulness of replies is improved compared to rule-based systems, but the responses become short and simple, and inconsistent across similar queries
Solution Approach 1:
The patent segments the response generation process into two independent components: a neural conversation model that generates concise, consistent responses, and a response template system that expands and enriches these responses. The neural model handles the consistent core response while templates handle the variability and completeness, resolving the contradiction between consistency and versatility.
Solution Approach 2:
The patent embeds the neural conversation model within a larger system that includes response templates, expansion modules, and selection mechanisms. The neural model generates a core response that is then nested within and expanded by multiple template layers, allowing the system to achieve both the consistency of the neural model and the completeness of the template system.
2Ease of manufacture
If hand-crafted rules are used to parse user messages, then the system is easy to implement, but it fails to handle variations in user queries and provides limited responses
Solution Approach 1:
The patent replaces the mechanical hand-crafted rule parsing system with a neural conversation model that uses pattern recognition and semantic understanding. This substitution maintains ease of implementation while dramatically improving the ability to handle query variations, as the neural model can generalize from training data rather than requiring explicit rules for each variation.
3Reliability
If a neural conversation model generates replies, then more useful responses are produced, but the system becomes difficult to implement as a practical user interface
Solution Approach 1:
The patent introduces response templates as an intermediary layer between the neural conversation model and the final user interface. The neural model generates concise responses that feed into template systems, which then expand and format the responses for practical use. This intermediary simplifies the overall implementation by providing a structured framework that bridges the neural model's output with practical interface requirements.
Data Source
AI summary
Certain examples described herein provide methods and systems for implementing a conversational agent, e.g. to train a predictive model used by the conversational agent. In examples, text data representing agent messages from a dialogue database are clustered and the clusters are used to generate response templates for use by the conversational agent. The predictive model is trained on training data generated by selectively assigning response templates to agent messages from text dialogues. Examples enable a predictive model to be trained on high quality data sets that are generated automatically from a corpus of historical data. In turn, they enable a natural language interface to be efficiently provided.