Dialog System Using Two-Stage Translation Model for Goal Accuracy
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Solution Overview
Problem
Existing dialog systems often fail to accurately maintain the history of user interactions over multiple turns, leading to errors in identifying user goals, particularly when constraints are updated, due to limitations in retaining dialog history and small training datasets.
Innovation Solution
A two-stage machine-translation model is enhanced by adding a second stage that computes probability distributions for output words, allowing the system to better represent user goals by incorporating candidate probabilities and improving the accuracy of dialog history retention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a basic machine-translation model is used for dialog processing, then the system structure is simple, but the accuracy of maintaining dialog history and identifying user goals deteriorates
Solution Approach 1:
The dialog processing system is segmented into two distinct stages: a translation model stage that handles language translation and a language model stage that handles dialog history retention and goal identification. This segmentation allows each stage to specialize in specific functions, improving overall accuracy without requiring a complete redesign of the system architecture.
Solution Approach 2:
The patent introduces a new dimensional layer to the processing architecture by adding the language model stage that operates in parallel with the translation model. This additional dimension enables the system to process both translation and dialog context simultaneously, resolving the contradiction between simplicity and accuracy.
2Measurement precision
If dialog history is retained over multiple turns, then the accuracy of identifying user goals improves, but the complexity of processing and storing history increases
Solution Approach 1:
The language model acts as an intermediary component that receives input from the translation model and processes dialog history information. This intermediary structure enables efficient handling of multi-turn dialog context without requiring direct complex interactions between all system components, thus managing complexity while improving goal identification accuracy.
3Measurement precision
If a two-stage model with probability distributions is added, then the accuracy of representing user goals improves, but the computational complexity increases
Solution Approach 1:
The translation model stage performs preliminary processing of input text and generates probability distributions for possible translations before the language model stage processes them further. This preliminary action reduces the computational burden on the second stage by pre-processing and filtering information, thereby managing overall computational complexity while maintaining improved accuracy.
Data Source
AI summary
A goal-oriented dialog system interacts with a user over one or more turns of dialog to determine a goal expressed by the user; the dialog system may then act to fulfill the goal by, for example, calling an application-programming interface. The user may supply dialog via text, speech, or other communication. The dialog system includes a first trained model, such as a translation model, to encode the dialog from the user into a context vector; a second trained model, such as another translation model, determines a plurality of candidate probabilities of items in a vocabulary. A language model determines responses to the user based on the input from the user, the context vector, and the plurality of candidate probabilities.