Attention Vector Dialog History Encoding for Reply Accuracy
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Solution Overview
Problem
Deep learning-based dialog systems struggle to effectively process and utilize dialog history information, leading to reply statements that may not meet user requirements and result in low dialog quality.
Innovation Solution
A method that involves encoding a to-be-replied statement into a vector, composing an attention vector using the vector and a decoding status vector, interacting this attention vector with dialog history statements to extract relevant information, and generating a to-be-decoded vector for word-by-word decoding of the reply statement, thereby incorporating historical context into the response.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If dialog history information is not effectively processed and utilized, then the system complexity is reduced, but the dialog quality and user requirement satisfaction deteriorate
Solution Approach 1:
The patent introduces an attention mechanism as an intermediary component that selectively processes dialog history information. The attention vector acts as a mediator between the encoding module and decoding module, allowing the system to focus on relevant historical context without processing all history uniformly, thus improving dialog quality while controlling complexity
Solution Approach 2:
The patent transforms dialog history information into vector representations and uses attention scores to dynamically change the weighting parameters of different historical statements. By adjusting the attention parameters based on relevance to current input, the system effectively utilizes dialog history without being overwhelmed by its complexity
2Reliability
If dialog history information is effectively processed and utilized, then the dialog quality improves, but the system complexity increases
Solution Approach 1:
The patent extracts only the relevant information from dialog history through the attention mechanism. Instead of processing all dialog history equally, the attention vector extracts and weights only those historical statements that are relevant to the current input, reducing the effective processing load while maintaining dialog quality
Solution Approach 2:
The patent segments dialog history into individual statements that are independently encoded into vectors. Each historical statement is processed separately and then selectively combined through the attention mechanism, allowing efficient processing of long dialog histories without overwhelming system complexity
3Speed
If the reply statement is generated only based on the to-be-replied statement, then the processing speed is faster, but the accuracy and user requirement satisfaction deteriorate
Solution Approach 1:
The patent performs preliminary encoding of dialog history statements into vectors before the decoding process. This preliminary action prepares the historical context in advance, allowing the attention mechanism to quickly retrieve and weight relevant information during decoding, thus maintaining processing speed while improving accuracy
Solution Approach 2:
The attention mechanism provides feedback to the decoding process by dynamically adjusting the weighting of historical information based on the current decoding state. The attention vector is updated at each decoding step to reflect the most relevant historical context, improving reply accuracy without significantly impacting processing speed
Data Source
AI summary
A deep learning based dialog method, apparatus, and device are provided and belong to the field of artificial intelligence. The method includes: obtaining a to-be-replied statement, encoding the to-be-replied statement to obtain a first vector , where the first vector is a representation of the to-be-replied statement; obtaining dialog history information corresponding to the to-be-replied statement, and the attention vector is used to represent search intent; making each dialog statement interact with the attention vector, so as to extract information related to the search intent from the dialog statement to obtain a plurality of result vectors, generating a to-be-decoded vector based on the plurality of result vectors, and decoding the to-be-decoded vector to obtain a next word in the reply statement. In the method, the reply statement refers to a dialog history.