Shared Encoder for NLU Processing Efficiency
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Solution Overview
Problem
Existing speech processing systems are inefficient in terms of resources and time, particularly during runtime and training operations, due to the need for multiple encoder-decoder pairs for different tasks.
Innovation Solution
The use of a shared encoder with multiple decoders, where the shared encoder processes input data to determine encoded representation data, which is then processed by decoders to perform different speech processing tasks, along with a task-specific attention-fusion architecture to adapt models for particular tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple encoder-decoder pairs are used for different speech processing tasks, then task-specific processing capability is improved, but resource consumption and processing time increase
Solution Approach 1:
The patent implements a shared encoder that serves multiple speech processing tasks simultaneously. The encoder is designed to process different input types (audio signals, text inputs) and generate encoded representations that can be utilized by various downstream tasks including speech recognition, natural language understanding, and language translation, thereby achieving multi-functionality and reducing the need for separate encoder-decoder pairs for each task
Solution Approach 2:
The patent combines multiple encoder functions into a single shared encoder architecture. By merging the encoding capabilities needed for different tasks into one unified component, the system reduces redundancy and resource consumption while maintaining the ability to perform multiple speech processing functions through a consolidated structure
2Measurement precision
If multiple encoder-decoder pairs are used for different tasks, then task accuracy is improved, but training time and computational resources increase
Solution Approach 1:
The patent performs preliminary encoding by processing inputs through the shared encoder to generate encoded representations that are then reused across multiple tasks. This preliminary action of encoding once and reusing the representations reduces redundant processing and training time while maintaining task accuracy, as the core encoding work is done only once rather than repeatedly for each task
Solution Approach 2:
The patent combines the training process for multiple tasks into a unified framework where the shared encoder is trained to serve multiple purposes. By merging the training objectives and processes, the system achieves task accuracy comparable to multiple separate models while significantly reducing total training time and computational resource requirements
3Reliability
If pre-trained networks are fully adapted for specific tasks, then task performance is improved, but resource usage and training time increase
Solution Approach 1:
The patent applies local quality by selectively adapting only the portions of the pre-trained shared encoder that are specific to each task, while preserving the general encoding capabilities. This approach allows task-specific optimization without requiring full retraining of the entire network, thereby improving task performance while maintaining efficient resource usage by leveraging the pre-trained components
Data Source
AI summary
Techniques for using a shared encoder and multiple different decoders for natural language understanding (NLU) tasks are described. The individual decoders are configured to perform different tasks using the output from one shared encoder. The decoders can process with respect to different domains and different languages. Using the shared encoder can reduce computation time during runtime. Using the shared encoder can reduce training costs (e.g., time and resources) when the system is updated to incorporate additional intents and entities. The system employs an attention mechanism to extract encoded representation data that can be used by the different decoders for its specific task.