Contractive Paraphrasing for Cross-Domain Semantic Parsing
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Solution Overview
Problem
Traditional systems for recognizing natural language utterances are limited to specific task domains and require extensive rule-based training for new applications, leading to high development costs and inefficiencies.
Innovation Solution
Utilizing a pre-trained, large-scale natural language model with a constrained decoder to translate utterances into structured representations, employing grammatical rules to generate canonical utterances that can be efficiently compiled into instructions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional rule-based systems are used for natural language recognition, then the system can accurately recognize predefined utterances in specific task domains, but the development cost and complexity increase significantly when applying to new task domains
Solution Approach 1:
The patent applies universality by using a single pre-trained language model that can handle multiple task domains through prompt engineering. Instead of developing separate rule-based systems for each domain, the same model processes different tasks by receiving appropriately crafted prompts containing task descriptions and exemplar input-output pairs, making the system universally applicable across domains without increasing development complexity
Solution Approach 2:
The patent applies preliminary action by pre-training the language model on extensive general language data before deployment. This pre-training phase prepares the model with broad language understanding capabilities, so that when deployed for specific tasks, it already possesses the foundational knowledge needed to accurately recognize and process utterances across various domains with minimal additional configuration
2Adaptability or versatility
If extensive rule-based training is conducted for new task domains, then the system can adapt to new applications, but the development time and cost increase
Solution Approach 1:
The system achieves adaptability to new task domains through prompt engineering rather than retraining. By constructing prompts with task-specific descriptions and exemplars, the same pre-trained model can adapt to different domains instantly, eliminating the time-consuming retraining process while maintaining versatility across applications
Solution Approach 2:
The patent introduces prompts as an intermediary between the pre-trained model and specific task domains. These prompts act as mediators that translate diverse task requirements into a format the model can process, enabling rapid adaptation to new domains without direct retraining by serving as a flexible interface layer
3Adaptability or versatility
If pre-trained large-scale natural language models are used, then the system can generate instructions across wide ranges of tasks without extensive re-training, but the model complexity and computational requirements increase
Solution Approach 1:
The patent leverages the inherent universality of pre-trained large-scale language models to cover wide ranges of tasks. These models possess broad language understanding capabilities that enable them to handle diverse tasks across multiple domains simultaneously, achieving extensive task coverage while accepting the necessary model complexity as a trade-off for versatility
4Reliability
If conventional semantic parsing methods are used, then the system can process specific task domains, but extensive manual rule creation is required for each new domain
Solution Approach 1:
The patent replaces manual mechanical rule creation with automated language model-based semantic parsing. Instead of manually crafting parsing rules for each domain, the system uses the pre-trained model's language understanding capabilities to automatically generate accurate semantic parses from prompts, dramatically simplifying the development process while maintaining high accuracy
Data Source
AI summary
Systems and methods are provided for automatically generating a program based on a natural language utterance using semantic parsing. The semantic parsing includes translating a natural language utterance into instructions in a logical form for execution. The methods use a pre-trained natural language model and generate a canonical utterance as an intermediate form before generating the logical form. The natural language model may be an auto-regressive natural language model with a transformer to paraphrase a sequence of words or tokens in the natural language utterance. The methods generate a prompt including exemplar input/output pairs as a few-shot learning technique for the natural language model to predict words or tokens. The methods further use constrained decoding to determine a canonical utterance, iteratively selecting sequence of words as predicted by the model against rules for canonical utterances. The methods generate a program based on the canonical utterance for execution in an application.