Joint Semantic Frame Parsing for Multi-Domain SLU Accuracy
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Solution Overview
Problem
Conventional modular design approaches for spoken language understanding (SLU) in digital personal assistants require separate training for each domain, leading to data fragmentation, error propagation, and user dissatisfaction due to unclear domain coverage, hindering scalability and accuracy.
Innovation Solution
A joint multi-domain recurrent neural network (JRNN) is employed to integrate domain classification, intent determination, and slot filling, enabling data sharing and reinforcement across multiple domains, using bi-directional recurrent neural networks (bRNN) and long-short term memory (RNN-LSTM) architectures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If separate domain models are trained for each domain in conventional modular design, then domain-specific accuracy can be maintained, but data fragmentation occurs and scalability is hampered
Solution Approach 1:
The patent combines multiple domain-specific models into a single multi-domain model that can handle multiple domains simultaneously. This unified model shares underlying representations and features across domains, eliminating data fragmentation while maintaining domain-specific accuracy through learned domain adaptations.
Solution Approach 2:
The multi-domain model is designed to be universal across multiple domains by learning shared semantic representations that can be adapted to different domains. This single model performs multiple functions (handling multiple domains) rather than requiring separate specialized models for each domain.
2Device complexity
If traditional pipelining of tasks is used in conventional modular design, then task separation is clear, but errors are transferred from one task to following tasks resulting in low-quality results
Solution Approach 1:
The patent merges domain classification, intent determination, and slot filling into a single integrated model that processes these tasks jointly. This eliminates the error propagation inherent in sequential pipelining because all tasks benefit from shared contextual understanding and mutual reinforcement rather than sequential error accumulation.
3Measurement precision
If manual fine tuning of parameters is performed for each domain in conventional modular design, then domain-specific optimization is achieved, but the process is error-prone and requires careful engineering for consistency
Solution Approach 1:
The patent uses parameter sharing and joint training across domains, where the model learns domain-specific adaptations through learned parameters rather than manual fine-tuning. This automated parameter adaptation maintains consistency across domains while achieving domain-specific optimization through the shared multi-domain representation.
4Reliability
If no data or feature sharing is done between individual domain models in conventional modular design, then domain independence is maintained, but data fragmentation significantly hampers scalability
Solution Approach 1:
The patent merges multiple domain models into a unified multi-domain model that shares data and features across domains. This shared representation enables scalability by allowing the model to leverage data from multiple domains simultaneously while maintaining the ability to handle domain-specific tasks through learned domain adaptations.
Data Source
AI summary
A processing unit can train a model as a joint multi-domain recurrent neural network (JRNN), such as a bi-directional recurrent neural network (bRNN) and/or a recurrent neural network with long-short term memory (RNN-LSTM) for spoken language understanding (SLU). The processing unit can use the trained model to, e.g., jointly model slot filling, intent determination, and domain classification. The joint multi-domain model described herein can estimate a complete semantic frame per query, and the joint multi-domain model enables multi-task deep learning leveraging the data from multiple domains. The joint multi-domain recurrent neural (JRNN) can leverage semantic intents (such as, finding or identifying, e.g., a domain specific goal) and slots (such as, dates, times, locations, subjects, etc.) across multiple domains.