Context-Based Multi-Turn Dialogue Matching via Sentence Segmentation
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Solution Overview
Problem
Existing multi-turn dialogue systems face challenges in achieving sufficient matching between context and answer sequences with different characteristics, leading to inefficient selection of the best answer in human-machine dialogue systems.
Innovation Solution
A context-based multi-turn dialogue method that involves obtaining historical dialogue information, performing word feature extraction to obtain word embeddings, and using partial semantic relationship matching with cross-attention mechanisms to align and calculate matching probabilities, thereby enhancing the matching between context and answer sequences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If sequence-based method connects context into a long sequence, then the model can capture global context information, but the matching between context and answer becomes insufficient when they have different characteristics
Solution Approach 1:
The patent segments the context into multiple sentence-level units and processes each sentence separately through individual encoders, rather than treating the entire context as a single long sequence. This segmentation allows for better preservation of local semantic characteristics while reducing information loss, and the segmented representations are then aggregated to form the overall context representation for matching with the answer.
Solution Approach 2:
The patent introduces a hierarchical dimension to the context representation, moving from a flat long-sequence approach to a multi-level structure where sentence-level embeddings are first computed, then aggregated to form discourse-level representations. This dimensional transformation enables the model to capture both local sentence meanings and global context relationships, improving the matching accuracy between context and answer.
2Device complexity
If hierarchy-based method models each sentence separately, then the computational complexity is reduced, but the interaction between sentences is insufficiently captured
Solution Approach 1:
The patent merges the advantages of both sequence-based and hierarchy-based methods by combining sentence-level separate modeling with discourse-level aggregation. Each sentence is modeled independently by its own encoder to reduce computational complexity, then the sentence embeddings are aggregated through attention mechanisms and pooling operations to capture sentence interactions at the discourse level, thus preserving interaction information without excessive complexity.
Solution Approach 2:
The patent introduces sentence embeddings as intermediary representations between individual sentences and the overall context. These embeddings serve as mediators that capture both local sentence semantics and global context relationships, enabling the model to capture sentence interactions without directly processing the entire context as a single sequence, thus balancing computational efficiency with interaction capture.
3Ease of manufacture
If existing models are used for multi-turn dialogue, then implementation is straightforward, but the matching between context and answer is insufficient when they have different characteristics
Solution Approach 1:
The patent changes the parameters of context representation by introducing multiple aggregation methods (max pooling, mean pooling, attention-based aggregation) and different embedding dimensions. These parameter changes allow the model to adapt to different context-answer matching scenarios, improving precision while maintaining implementation feasibility through configurable parameters rather than requiring complete model redesign.
Data Source
AI summary
The present disclosure discloses a context-based multi-turn dialogue method. The method includes: obtaining to-be-matched historical dialogue information; performing a word feature extraction based on the to-be-matched historical dialogue information to obtain a historical dialogue word embedding; obtaining candidate answer information; performing the word feature extraction based on the candidate answer information to obtain a candidate answer word embedding; obtaining a historical dialogue partial matching vector and a candidate answer partial matching vector by performing partial semantic relationship matching based on the historical dialogue word embedding and the candidate answer word embedding; obtaining a candidate answer matching probability by performing a matching probability calculation based on the historical dialogue partial matching vector and the candidate answer partial matching vector; and determining matched answer information based on the candidate answer information and the candidate answer matching probability.