Natural Language Sequence Generation for Customer Service
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Solution Overview
Problem
Existing language models and customer service technologies fail to accurately understand and respond to customer satisfaction in real-time, as they are not trained on customer service data and require manual user input for sentiment analysis and natural language sequence generation.
Innovation Solution
A system that uses fine-tuned machine learning models, such as Extreme Gradient Boosting and modified UMLFit, to attribute natural language utterances to customers or agents and generate real-time satisfaction scores, enabling the automatic generation of natural language sequences for customer service agents to improve customer satisfaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing language models are used for customer service, then general language understanding is provided, but accurate real-time customer satisfaction analysis is not achieved
Solution Approach 1:
The language model is fine-tuned in advance on customer service transcripts and satisfaction data before deployment. This preliminary training action enables the model to accurately analyze customer satisfaction in real-time during actual customer service interactions without requiring complex processing during the interaction itself.
Solution Approach 2:
The system continuously learns from customer service interactions by incorporating satisfaction scores and interaction outcomes back into the training data. This feedback mechanism progressively improves the model's accuracy in analyzing customer satisfaction while maintaining real-time performance through iterative refinement.
2Extent of automation
If manual user input is required for sentiment analysis, then detailed analysis is possible, but automation and efficiency are reduced
Solution Approach 1:
The system performs sentiment analysis automatically without requiring manual user input. The fine-tuned language model independently processes customer service transcripts, attributes utterances to customers or agents, generates satisfaction scores, and produces natural language sequences autonomously, eliminating the need for manual sentiment analysis while maintaining high accuracy.
3Productivity
If existing models are used, then general language processing is provided, but automatic natural language sequence generation based on customer satisfaction is not achieved
Solution Approach 1:
The language model's parameters are fine-tuned specifically for customer service contexts by training on domain-specific transcripts and satisfaction data. This parameter adjustment enables the model to rapidly generate context-appropriate natural language sequences that adapt to varying customer satisfaction levels and service scenarios while maintaining high generation speed.
Data Source
AI summary
Various embodiments discussed herein are directed to improving existing technologies by generating a natural language sequence, which is a candidate for a first person to utter or not utter at least partially responsive to and based on a detected natural language utterance of a second person. A first score indicative of customer satisfaction is determined based on the content of the detected natural language utterance and learning patterns or associations within historical transcripts between, for instance, a customer and a customer service agent. Based on the level of customer satisfaction, the natural language sequence is generated.