Dialog Generation Using Encoder-Decoder for Out-of-Vocabulary Words
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Solution Overview
Problem
Current dialog systems are limited in generating responses as they can only select words and phrases from a preset dictionary, resulting in restricted content output, as all words and phrases in their answers come from a fixed dictionary, leading to limited conversation capabilities.
Innovation Solution
A dialog generation method and apparatus that uses a neural network model to encode input dialog sequences and associated information, allowing for the generation of output dialog sequences that include out-of-vocabulary words by combining factual and opinion information, enabling the system to dynamically generate sentences that include facts and opinions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a preset dictionary is used for generating dialog responses, then the system structure remains simple and manageable, but the content diversity and information richness of responses are limited
Solution Approach 1:
The dialog generation process is segmented into distinct functional modules: an encoder that processes input sequences, a decoder that generates output sequences, and an information integration component that combines factual and opinion information. This segmentation allows the system to handle complex tasks while maintaining manageable module structures.
Solution Approach 2:
The system transitions from a static preset dictionary approach to a dynamic neural network generation approach. The encoder-decoder model dynamically generates responses based on input context, enabling the system to produce diverse and context-appropriate responses rather than selecting from fixed predefined options.
2Loss of information
If only words from a fixed dictionary are used in answers, then the system maintains consistency and predictability, but the information richness and factual accuracy are reduced
Solution Approach 1:
The system merges multiple information sources including factual information, opinion information, and contextual dialog history. By integrating these diverse information streams through the encoder-decoder framework, the system produces responses that are both information-rich and contextually consistent.
Solution Approach 2:
The system changes the fundamental parameter of vocabulary selection from a fixed discrete set to a continuous probabilistic generation process. The neural network models generate words and phrases by predicting probability distributions over the vocabulary, allowing flexible information expression while maintaining linguistic coherence through learned patterns.
Data Source
AI summary
The present disclosure provides a dialog generation method, performed by a human-machine dialog system. The method includes obtaining an input dialog sequence from a dialog client; obtaining associated information related to the input dialog sequence; encoding, by an encoder, the input dialog sequence to obtain an input encoding vector; encoding, by the encoder, the associated information to obtain an associated encoding vector; decoding, by a decoder, the input encoding vector and the associated encoding vector to obtain an output dialog sequence, the output dialog sequence comprising an out-of-vocabulary word corresponding to the associated information; and transmitting the output dialog sequence to the dialog client.