Time-Sequence Feature Screening Encoding Module for Multi-Turn Conversation
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Solution Overview
Problem
Multi-turn human-machine conversation systems face challenges in identifying and screening semantic information from historical conversations, as the sequence of utterances is crucial for understanding context, and existing methods fail to effectively extract time-sequence features, leading to low prediction accuracy.
Innovation Solution
A method and apparatus utilizing a time-sequence feature screening encoding module that processes historical conversation data with a pre-training language model like BERT, employing an encoder and attention mechanism to extract semantic features and time-sequence information, improving the accuracy of response prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If semantic information screening is performed on historical conversation utterances, then prediction accuracy is improved, but device complexity increases due to the need for time-sequence feature extraction and encoding modules
Solution Approach 1:
The model is divided into distinct functional modules: a pre-training model embedding module for initial encoding, a time-sequence feature screening encoding module for sequential processing, and a label prediction module for output. This segmentation allows each module to specialize in specific tasks, improving overall prediction accuracy while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The time-sequence feature screening encoding module acts as an intermediary between the pre-training embedding module and the label prediction module. It processes the embedding representations by extracting and screening time-sequence features, filtering out irrelevant information while preserving temporal patterns, thus enabling accurate prediction without requiring the entire model to be overly complex.
2Loss of information
If time-sequence features are extracted from historical conversations, then semantic representation completeness is improved, but processing time increases
Solution Approach 1:
The pre-training model embedding module performs preliminary encoding of historical conversation utterances before the time-sequence feature extraction. By pre-computing embedding representations, the system reduces the processing burden in subsequent time-sequence processing steps, maintaining semantic representation completeness while minimizing overall processing time.
Solution Approach 2:
The time-sequence feature screening encoding module selectively extracts only the relevant time-sequence features from the embedding representations. Using attention mechanisms and screening criteria, it identifies and extracts only the temporal patterns necessary for accurate prediction, discarding redundant information and reducing processing time compared to processing all possible features.
Data Source
AI summary
Disclosed is a multi-turn human-machine conversation method and apparatus based on a time-sequence feature screening encoding module, belonging to the technical field of natural language processing and artificial intelligence. The technical problem to be solved by the disclosure is how to screen information for each utterance in a historical conversation so as to obtain semantic information only relevant to candidate responses and how to reserve and extract time-sequence features in the historical conversation, thus improving prediction accuracy of a multi-turn human-machine conversation system. The adopted technical scheme is as follows: S1, acquiring a multi-turn human-machine conversation data set; S2, constructing a multi-turn human-machine conversation model: constructing a multi-turn human-machine conversation model based on the time-sequence feature screening encoding module; and S3, training the multi-turn human-machine conversation model: training the multi-turn human-machine conversation model constructed in S2 on the multi-turn human-machine conversation data set obtained in S1.