Chatbot Response Generation via Topic Classification and Sentiment Weighting
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Solution Overview
Problem
Current chatbot and human-computer interaction systems rely on static responses from document repositories, which are insufficiently adaptive to user-specific needs, sentiments, and topics, particularly when incorporating humor, leading to unmet needs in dynamic and personalized interactions.
Innovation Solution
A method that classifies interaction topics using a logistic regression model, extracts relevant documents, scores sentiments with a generative adversarial model, weights interaction types and sentiments using an attention layer, and adjusts documents with a natural language generation transformer model to provide contextually relevant and personalized responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If static responses from document repositories are used, then system simplicity is maintained, but adaptability to user-specific needs and sentiments deteriorates
Solution Approach 1:
The system transitions from static document retrieval to dynamic response generation by implementing a chatbot architecture that adapts to user-specific needs, topics, and sentiments in real-time through multiple processing layers including classification, document extraction, sentiment scoring, and natural language generation
Solution Approach 2:
The system is divided into distinct functional modules: topic classification layer, document extraction layer, sentiment scoring layer, weighting layer, and natural language generation layer. Each layer processes specific aspects of user interaction independently, allowing the system to handle complexity through modular decomposition
2Productivity
If multiple processing layers are added to improve personalization, then user engagement improves, but processing time increases
Solution Approach 1:
Documents are pre-processed and stored in a structured repository with metadata tags before interaction. The system pre-classifies available documents and organizes them by topic and sentiment, enabling faster retrieval during actual user interactions without requiring full processing from scratch
Solution Approach 2:
Traditional sequential processing is replaced with parallel processing across multiple layers. The topic classification, document extraction, and sentiment scoring operations occur simultaneously rather than sequentially, reducing overall processing time while maintaining personalization quality
3Adaptability or versatility
If contextually relevant responses are generated, then user-specific needs are met, but response dynamicity increases system complexity
Solution Approach 1:
The system employs a universal natural language generation model that can produce different types of responses (informational, humorous, empathetic) based on input parameters rather than requiring separate specialized models for each response type. This multi-functional approach reduces overall system complexity while maintaining contextual relevance
Data Source
AI summary
Using a logistic regression classification model executing on a processor, a topic is classified into an interaction type in a set of predefined interaction types. A set of documents corresponding to the topic is extracted from a document repository. Using a generative adversarial model executing on a processor, a sentiment corresponding to a reaction to a previous presentation is scored, the scoring resulting in a scored sentiment. Using a trained attention layer model, the interaction type, the set of documents, and the scored sentiment are weighted, the weighting generating a weighted interaction type, a weighted set of documents, and a weighted scored sentiment. Using a natural language generation transformer model executing on the processor, a document in the weighted set of documents is weighted according to the weighted interaction type and the weighted scored sentiment.