Dialogue System Response Generation via Keyword Relevance
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Solution Overview
Problem
Retrieval-based dialogue systems often fail to meet users' information needs, even when they possess relevant information, as they only provide answers based on exact question-answer pairs without offering related or alternative information.
Innovation Solution
A computer-implemented method that generates similarity scores for question-answer pairs, classifies them as relevant or irrelevant, and uses machine learning to provide responses that include information associated with question keywords not present in the query, offering relevant or alternative information when no exact match is found.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the dialogue system provides only exact question-answer pair matches, then the system maintains simplicity and fast response, but the system fails to meet users' information needs when no exact match exists
Solution Approach 1:
The patent segments the response generation process into multiple stages: first identifying exact question-answer pair matches, then separately identifying relevant question-answer pairs that contain useful information even without exact matches. This segmentation allows the system to maintain fast exact matching while adding information retrieval capabilities through the separate relevance identification stage.
Solution Approach 2:
The patent applies partial action by implementing only the necessary portion of full semantic analysis - specifically identifying relevant question-answer pairs based on keyword matching and relevance criteria, rather than performing complete natural language understanding. This partial approach provides improved information retrieval while avoiding the computational complexity of full semantic analysis.
2Loss of information
If the dialogue system retrieves and analyzes multiple question-answer pairs for relevance, then the system improves information retrieval capability, but the system complexity increases
Solution Approach 1:
The patent applies local quality by implementing different processing strategies for different types of queries: exact matches receive simple direct retrieval, while non-exact matches receive enhanced relevance analysis. This localized approach to quality control allows the system to improve information retrieval for relevant cases without increasing complexity for all cases uniformly.
Solution Approach 2:
The patent changes the parameter of similarity matching from strict exact matching to flexible relevance matching based on keyword overlap and contextual relevance. This parameter change allows the system to retrieve relevant information with varying degrees of match strength, improving information retrieval while managing complexity through adjustable matching criteria.
3Measurement precision
If the dialogue system only provides answers from exact matches, then the system maintains high precision in response accuracy, but the system loses adaptability to handle varied user queries
Solution Approach 1:
The patent introduces dynamics by making the response selection process adaptive rather than static. The system dynamically adjusts between providing exact match answers and providing relevant information based on whether an exact match exists. This dynamic behavior allows the system to maintain high accuracy for exact matches while gaining versatility to handle varied user queries through relevant information retrieval.
Solution Approach 2:
The patent applies universality by designing a dual-function response generation mechanism that can handle both exact match retrieval and relevant information retrieval. This multi-functional approach allows the same system to serve both precise answer provision and flexible information retrieval needs, enhancing adaptability without sacrificing the core exact matching capability.
Data Source
AI summary
A computer-implemented system and method relate to natural language processing. A candidate is selected based on a query. The candidate includes a question and an answer to that question. The candidate is classified as belonging to a class. The class indicates that the candidate is relevant to the query. The class is selected from among a group of classes, which includes at least that class and another class. The another class indicates that the candidate has the answer to the query. Upon classifying the candidate as belonging to the class, a machine learning system is configured to generate a relevant response for the query upon receiving the query and the question as input data. The relevant response provides information associated with a keyword of the question that is not contained in the query.