Multi-turn Dialogue Retrieval via Context-Reply Consistency Matching
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Solution Overview
Problem
Current question answering systems face challenges in efficiently retrieving multi-turn dialogues due to limitations in matching context features with candidate reply features, leading to suboptimal response accuracy and user experience.
Innovation Solution
A method is introduced that involves obtaining present question and context information, generating context and candidate reply features, calculating consistency information between these features, and selecting the most suitable candidate reply based on this information, utilizing a combination of deep learning models like multilayer RNN and consistency matching models trained on external dialogue knowledge databases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional question answering systems are used for multi-turn dialogue retrieval, then the system structure is simple, but the response accuracy and user experience deteriorate due to inability to effectively match context features with candidate reply features
Solution Approach 1:
The patent segments the dialogue retrieval process into distinct components: context feature extraction, candidate reply feature extraction, and consistency information calculation. Each component is handled by specialized modules (context feature extraction module, candidate reply feature extraction module, consistency information calculation module), allowing for precise processing of each aspect while maintaining overall system organization.
Solution Approach 2:
The patent introduces consistency information as an intermediary element that bridges the gap between context features and candidate reply features. This consistency information serves as a mediator that quantifies the matching degree, enabling more accurate selection of appropriate replies in multi-turn dialogues without requiring direct complex interactions between all system components.
2Adaptability or versatility
If simple matching methods are used between context and candidate replies, then the system complexity is low, but the matching degree and dialogue coherence worsen
Solution Approach 1:
The patent transforms the matching problem from a qualitative assessment to a quantitative parameter-based evaluation. By extracting specific features from context and candidate replies, and calculating consistency information as a measurable parameter, the system can objectively assess matching degrees and select the most appropriate replies, significantly improving dialogue coherence.
Solution Approach 2:
The patent extends the matching process from a single-dimensional comparison to a multi-dimensional feature space. By extracting multiple features from both context and candidate replies (such as semantic features, contextual features, and linguistic features), the system evaluates matching across multiple dimensions, leading to more nuanced and coherent dialogue responses.
3Reliability
If multi-turn dialogue retrieval is not optimized, then the system structure remains simple, but the user experience and response relevance deteriorate
Solution Approach 1:
The patent performs preliminary actions by pre-extracting context features and candidate reply features before the actual dialogue response selection. The context feature extraction module and candidate reply feature extraction module prepare the necessary feature representations in advance, enabling faster and more accurate response selection during actual dialogue interactions, thereby improving user experience.
Solution Approach 2:
The consistency information calculation module provides feedback by quantifying the matching degree between context and candidate replies. This feedback mechanism allows the system to evaluate and compare multiple candidate replies, selecting the one with the highest consistency, thus ensuring reliable and relevant responses that enhance user experience in multi-turn dialogues.
Data Source
AI summary
A method for retrieving a multi-turn dialogue, a storage medium, and an electronic device are provided. The detailed implementation includes: obtaining present question information and context information of the present question information; obtaining a plurality of pieces of candidate reply information of the present question information; generating a context feature based on the context information, and generating a plurality of candidate reply features based on the plurality of pieces of candidate reply information; obtaining consistency information between the context feature and each of the plurality of candidate reply features; selecting a piece of candidate reply information from the plurality of pieces of candidate reply information for replying based on the consistency information between the context feature and each of the plurality of candidate reply features.