Markov Network Model for Limited Supervision Question Answering
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Solution Overview
Problem
Conventional natural language semantic matching methods, such as deep learning and knowledge graph-based approaches, face challenges in adapting to diverse use-cases and are limited by the need for large amounts of training data from the same domain type, and often rely on incomplete knowledge bases.
Innovation Solution
A Markov Network model is trained using question-answer pairs with labeled correct or incorrect answers, allowing for semantic matching across different domain types, including specific and non-specific domains, by parsing object and relation texts to generate a Markov Network that identifies correct answers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning methods are used for semantic matching, then model accuracy can be improved, but the requirement for large amounts of domain-specific training data increases
Solution Approach 1:
The patent segments the training data into domain-specific question-answer pairs and domain-agnostic question-answer pairs. The Markov network is trained separately on these segments, allowing the model to learn domain-specific patterns while also acquiring general semantic matching capabilities from domain-agnostic data, thereby reducing the total volume of domain-specific training data required
Solution Approach 2:
The patent introduces domain-agnostic question-answer pairs as an intermediary training resource. These pairs serve as a bridge that provides general semantic matching patterns without requiring extensive domain-specific knowledge, enabling the model to achieve high accuracy with less domain-specific training data
2Adaptability or versatility
If knowledge graph-based semantic matching is used, then answer retrieval can be performed, but the completeness of the knowledge base limits the application scope
Solution Approach 1:
The patent enables the system to self-adapt to new domains by training the Markov network on domain-specific question-answer pairs. The model automatically learns domain-specific linguistic patterns and semantics without requiring manual construction of domain-specific knowledge bases, allowing the system to serve itself across diverse domains
Solution Approach 2:
The patent changes the fundamental parameter from relying on preconstructed knowledge base completeness to using learned semantic representations from training data. The Markov network learns probabilistic semantic relationships directly from question-answer pairs, making the system adaptable to any domain where training data is available rather than being constrained by knowledge base coverage
3Measurement precision
If deep learning models are trained on domain-specific data, then performance on that domain improves, but the model cannot adapt well to other domain types
Solution Approach 1:
The patent segments the training process into two distinct phases: training on domain-specific question-answer pairs to capture domain-specific patterns, and training on domain-agnostic question-answer pairs to capture general semantic matching capabilities. This segmentation allows the model to maintain high domain-specific accuracy while also developing cross-domain adaptability
Solution Approach 2:
The patent makes the Markov network multi-functional by enabling it to process both domain-specific and domain-agnostic training data. The model serves dual purposes: achieving high accuracy on specific domains through domain-specific training while also gaining universal semantic matching capabilities from domain-agnostic training, thereby functioning effectively across multiple domains
Data Source
AI summary
Methods for natural language semantic matching performed by training and using a Markov Network model are provided. The trained Markov Network model can be used to identify answers to questions. Training may be performed using question-answer pairs that include labels indicating a correct or incorrect answer to a question. The trained Markov Network model can be used to identify answers to questions from sources stored on a database. The Markov Network model provides superior performance over other semantic matching models, in particular, where the training data set includes a different information domain type relative to the input question or the output answer of the trained Markov Network model.