Low-Resource Semantic Parsing for Ontology-Span Alignment
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Solution Overview
Problem
Training effective Natural Language Understanding (NLU) models in low-resource settings is challenging due to the difficulty in establishing alignment between ontology labels and utterance spans without sufficient parallel data.
Innovation Solution
The solution involves leveraging the intrinsic properties of ontology labels and spans, encoded by strong language models, to provide an accurate representation of these labels, allowing the NLU model to be fine-tuned with a low-resource dataset for semantic understanding in unseen domains.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional NLU models are trained with sufficient parallel data to establish alignment between ontology labels and utterance spans, then measurement precision improves, but loss of substance increases due to data requirements
Solution Approach 1:
The system performs preliminary action by using strong language models to encode intrinsic properties of ontology labels and spans before training the NLU model. This pre-encoding creates a foundation that reduces the need for extensive parallel training data, as the models already possess contextual understanding from the encoding process.
Solution Approach 2:
The patent introduces an intermediary mechanism where strong language models act as mediators between raw text data and the NLU model. These models encode semantic information and intrinsic properties, serving as a bridge that reduces the direct dependency between training data volume and alignment accuracy.
2Adaptability or versatility
If NLU models are fine-tuned with low-resource datasets, then adaptability improves for unseen domains, but measurement precision deteriorates due to insufficient data
Solution Approach 1:
The system prepares in advance by encoding intrinsic properties of ontology labels and spans using strong language models before fine-tuning. This preliminary encoding ensures that even with limited domain-specific data, the model starts with a strong semantic foundation that maintains precision while adapting to new domains.
Solution Approach 2:
The patent changes parameters by utilizing encoded representations from strong language models as additional features or embeddings during fine-tuning. This parameter enhancement allows the model to achieve better adaptability without sacrificing precision, as the encoded intrinsic properties provide robust semantic signals even in low-resource settings.
Data Source
AI summary
In one embodiment, a method includes receiving training utterances associated with a domain, receiving ontology labels for the domain, wherein the ontology labels comprise one or more of an intent or a slot, generating an inventory for the domain, wherein the inventory comprises at least a respective index and respective span for each intent or slot, wherein the respective span comprises a respective descriptive label associated with the intent or slot, and wherein the respective descriptive label comprises a natural-language description of the intent or slot, generating frames for training utterances based on the training utterances and the inventory by a natural-language understanding (NLU) model, wherein each frame comprises a structural representation of the respective training utterance, wherein the structural representation is generated based on a comparison between the corresponding training utterance and the inventory, and updating the NLU model based on the frames.