Dialog Response Generation Using Topic-Guided Solution Segments
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current dialog systems are time-consuming and costly to develop, and data-driven models face challenges in learning language and business logic from chatlogs, making it difficult to identify and represent necessary external information effectively.
Innovation Solution
A processor-based method for solution-guided generation of responses in dialog systems, which identifies topics from user utterances, selects relevant solutions, and generates responses using a text classification model, sequence-to-sequence machine learning, and text generation AI models, leveraging document corpora and subject matter expertise to create efficient and labor-saving conversation flows.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If SME based modeling is used to create dialog flows, then the quality and accuracy of dialog responses is improved, but the development time and cost increase significantly
Solution Approach 1:
The patent uses chatlogs (real conversation records) as training data to copy and learn from actual dialog patterns, replacing the need for manual SME creation of dialog flows. The system learns language and business logic implicitly from these copied examples through machine learning models.
Solution Approach 2:
The system enables itself to learn from chatlogs and external knowledge sources automatically without requiring manual intervention from subject matter experts. The machine learning models self-train on available data to generate responses, reducing human labor involvement.
2Productivity
If data driven modeling from chatlogs is used, then development cost and time are reduced, but the difficulty of learning language and business logic increases
Solution Approach 1:
The patent segments the complex task of learning from chatlogs into distinct components: topic identification, solution identification, and response generation. Each component is handled by specialized models (text classification model for topics, sequence to sequence model for responses), making the overall system more manageable despite the complexity.
Solution Approach 2:
The patent introduces topic identification as an intermediary step between receiving user input and generating responses. This mediator extracts key information from chatlogs and structured data, simplifying the learning task for the sequence to sequence model by providing pre-processed features rather than raw data.
3Measurement precision
If external knowledge is integrated into dialog systems, then the accuracy of task completion is improved, but the complexity of identifying and representing information increases
Solution Approach 1:
The patent performs preliminary action by pre-processing chatlogs and external knowledge into structured formats before training the models. Topics and solutions are identified and organized in advance, creating a structured knowledge base that the sequence to sequence model can efficiently access during response generation.
Data Source
AI summary
According to one embodiment, a method, computer system, and computer program product for solution guided generation of responses for dialog systems is provided. The embodiment may include receiving, by a processor, first voice data associated with a first user utterance in conversation in a guided dialog system. The embodiment may include identifying from the first voice data a first topic of a set of topics associated with the first user utterance. The embodiment may include identifying a first solution associated with the first topic. The first solution having one or more solution segments for accomplishing a task related to the topic. The embodiment may include generating a first response for a second user based on a first solution segment of the first solution and the first voice data.