Knowledge Graph Multi-Alignment for Spoken Language Understanding
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Solution Overview
Problem
Conventional spoken language understanding (SLU) systems face challenges due to errors in automatic speech recognition (ASR) transcriptions and the loss of prosodic information, as well as the inability of language models to grasp world knowledge and relationships, leading to suboptimal performance in natural language processing tasks.
Innovation Solution
The development of a computing system that aligns multiple knowledge graphs to train a knowledge module, integrating it with a language module to generate an integrated knowledge-language module, which couples with a speech module for improved semantic analysis and natural language understanding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional ASR transcription is used to convert audio to text, then speech recognition is achieved, but transcription errors occur and prosodic information is lost
Solution Approach 1:
The patent merges ASR transcription results with knowledge graph data and prosodic feature extraction to create a unified representation that compensates for transcription errors and preserves prosodic information through multi-source information integration
Solution Approach 2:
The patent introduces knowledge graphs as an intermediary layer that bridges audio signals and language understanding, using entity relationships and world knowledge to correct transcription errors and maintain semantic accuracy despite information loss in direct transcription
2Adaptability or versatility
If language models are trained on large-scale unlabeled corpus, then self-supervised training capability is achieved, but the models struggle to grasp world knowledge and relationships
Solution Approach 1:
The patent combines self-supervised language model training with knowledge graph integration, merging the strengths of both approaches where the language model provides adaptive learning capability while the knowledge graph supplies structured world knowledge and entity relationships for improved reliability
3Adaptability or versatility
If knowledge-based data is integrated by combining token representations with pre-computed embeddings, then knowledge integration is achieved, but over-parameterization occurs and model training is halted
Solution Approach 1:
The patent extracts and separates knowledge graph data into a distinct module that interfaces with the language model through optimized embedding alignment, removing the problematic pre-computed embeddings that caused over-parameterization while retaining the beneficial knowledge integration capability
4Reliability
If the model is trained from the beginning for a new knowledge domain, then complete training is achieved, but training time and computational resources are excessive
Solution Approach 1:
The patent performs preliminary alignment of knowledge graph embeddings with the language model's token representations before domain-specific training, preparing the model in advance so that subsequent domain adaptation requires minimal retraining while maintaining high accuracy
Data Source
AI summary
A language module is joint trained with a knowledge module for natural language understanding by aligning a first knowledge graph with a second knowledge graph. The knowledge module is trained on the aligned knowledge graphs. Then, the knowledge module is integrated with the language module to generate an integrated knowledge-language module.