GAN Semantic Parser Training for Low-Data Dialog Systems
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Solution Overview
Problem
Conventional dialog systems require large amounts of training data to effectively train semantic parsers, leading to inefficiencies and errors when insufficient data is available, which can result in inappropriate responses.
Innovation Solution
Utilizing a generative adversarial network (GAN) to jointly train a semantic parser and a discriminator, where the semantic parser generates logical forms and the discriminator distinguishes between authentic and inauthentic logical forms, with an error-minimization module providing training signals to improve both models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional training methods are used with large amounts of training data, then the semantic parser can be trained effectively, but the data requirements become excessive and inefficiencies occur when data is insufficient
Solution Approach 1:
The patent applies preliminary action by pre-training the discriminator on authentic seed data before the main training process. This preliminary preparation of the discriminator enables it to better evaluate generated logical forms, allowing the semantic parser to learn more effectively from smaller datasets. The discriminator's pre-established understanding of authentic data patterns serves as a foundation for the adversarial training process.
Solution Approach 2:
The discriminator acts as an intermediary between the semantic parser and the ground truth data. Instead of directly comparing parser output to authentic examples, the discriminator mediates this comparison by evaluating whether generated logical forms resemble authentic ones. This intermediary mechanism enables more efficient learning from smaller datasets by providing nuanced feedback about the quality and authenticity of generated outputs.
2Reliability
If large amounts of training data are used, then training effectiveness improves, but training time and computational resources increase
Solution Approach 1:
The patent uses copying by creating synthetic training examples through the adversarial process. The semantic parser generates logical forms that copy the essential patterns and structures of authentic logical forms, as evaluated by the discriminator. This copying mechanism allows the system to learn from smaller datasets by generating additional training examples that replicate the characteristics of authentic data, reducing the need for large volumes of actual training data.
Solution Approach 2:
The patent applies parameter changes by dynamically adjusting the training objectives and loss functions during adversarial training. The discriminator's evaluation metrics and the parser's generation parameters are continuously refined based on the adversarial feedback loop. This dynamic parameter adjustment enables more efficient convergence with smaller datasets, reducing training time while maintaining or improving effectiveness compared to conventional fixed-parameter training approaches.
3Reliability
If the semantic parser is trained to generate logical forms, then the dialog system becomes more accurate, but the system complexity increases due to the GAN architecture
Solution Approach 1:
The patent applies universality by designing the discriminator to serve multiple functions: it acts as both an evaluator of generated logical forms and a source of training feedback for the semantic parser. The same discriminator architecture is used throughout training to consistently evaluate authenticity, providing multi-functional utility that justifies its inclusion. This multi-functionality reduces overall system complexity compared to having separate evaluation and training components.
Solution Approach 2:
The patent merges the training objectives of the semantic parser and discriminator into a unified adversarial training framework. Rather than training these components separately and then integrating them, the patent combines their training processes into a single coordinated system where gradients from the discriminator directly inform parser improvements. This merging reduces architectural complexity by eliminating the need for separate training pipelines and intermediate interfaces.
Data Source
AI summary
Disclosed herein are techniques for using a generative adversarial network (GAN) to train a semantic parser of a dialog system. A method described herein involves accessing seed data that includes seed tuples. Each seed tuple includes a respective seed utterance and a respective seed logical form corresponding to the respective seed utterance. The method further includes training a semantic parser and a discriminator in a GAN. The semantic parser learns to map utterances to logical forms based on output from the discriminator, and the discriminator learns to recognize authentic logical forms based on output from the semantic parser. The semantic parser may then be integrated into a dialog system.