Neural Slot Value Determination via Descriptor Embeddings
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Solution Overview
Problem
Current automated assistants face limitations in determining slot values for agent commands due to predefined slots not being applicable across various domains, leading to computationally burdensome interactions and the need for extensive training for new domains, which restricts their functionality and efficiency.
Innovation Solution
The method involves using natural language input embeddings and descriptor embeddings within a neural network model to determine slot values based on textual descriptors, allowing for adaptation across multiple domains without explicit schema alignment and reducing the need for extensive training data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If predefined slots are used for agent commands, then the automated assistant can process natural language input efficiently, but the slots may not be applicable to various domains and intents, limiting functionality
Solution Approach 1:
The patent applies universality by training a single neural network model to handle multiple domains and intents simultaneously. The model learns to map natural language inputs to slot values across different domains (e.g., restaurant reservations, flight bookings, weather queries) without requiring separate predefined slots for each domain, thus achieving both efficiency and adaptability
Solution Approach 2:
The patent changes the parameter representation from fixed predefined slots to dynamic slot descriptors generated by the neural network. The model learns optimal slot parameters (such as slot names, types, and constraints) based on the specific domain and intent, allowing the system to adapt its slot structure to different domains while maintaining processing efficiency
2Adaptability or versatility
If predefined slots are adapted to new domains, then new slot functionality can be achieved, but extensive computationally burdensome training is required
Solution Approach 1:
The patent applies preliminary action by pre-training the neural network model on a diverse dataset covering multiple domains and intents before deployment. This pre-training enables the model to learn generalizable slot value determination patterns, so that when deployed to new domains, it requires minimal additional training or adaptation, significantly reducing the computational cost of domain adaptation
Solution Approach 2:
The patent uses copying by leveraging the knowledge and patterns learned from training data across multiple domains. The neural network model copies effective slot value determination strategies from one domain to another through its learned representations, avoiding the need to retrain from scratch for each new domain and thus reducing computational requirements
3Measurement precision
If predefined slots are used, then slot value determination can be performed, but computationally burdensome excess interactions are required to resolve values already indicated by prior input
Solution Approach 1:
The patent applies feedback by implementing a dialog state tracker that maintains context information from prior user inputs. The neural network model uses this feedback to determine whether slot values have already been resolved in previous turns, eliminating the need for redundant interactions and reducing the time required to complete tasks while maintaining accurate slot value determination
Data Source
AI summary
Determining slot value(s) based on received natural language input and based on descriptor(s) for the slot(s). In some implementations, natural language input is received as part of human-to-automated assistant dialog. A natural language input embedding is generated based on token(s) of the natural language input. Further, descriptor embedding(s) are generated (or received), where each of the descriptor embeddings is generated based on descriptor(s) for a corresponding slot that is assigned to a domain indicated by the dialog. The natural language input embedding and the descriptor embedding(s) are applied to layer(s) of a neural network model to determine, for each of the slot(s), which token(s) of the natural language input correspond to the slot. A command is generated that includes slot value(s) for slot(s), where the slot value(s) for one or more of the slot(s) are determined based on the token(s) determined to correspond to the slot(s).