Neural Network Semantic Parsing via Canonical Forms
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Solution Overview
Problem
Existing semantic parsing methods, such as SPO, face challenges in accurately mapping natural language utterances to logical forms due to low oracle accuracy, where the retrieved list of logical forms often does not contain the correct one, leading to poor performance in domains with less than 50% accuracy.
Innovation Solution
A neural network model is trained to predict canonical forms for input text sequences, utilizing an encoder to generate representations based on n-grams and a decoder to sequentially predict words of the canonical form, improving the mapping process by directly converting natural language to canonical forms which can then be parsed to logical forms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the SPO method is used to map natural language utterances to logical forms, then the system can process complex questions, but the accuracy is less than 50% due to low oracle accuracy where the retrieved list of logical forms often does not contain the correct one
Solution Approach 1:
The patent introduces canonical forms as an intermediary representation between natural language utterances and logical forms. Instead of directly retrieving and ranking logical forms from a knowledge base, the system first generates canonical forms that capture the semantic structure, then maps these to logical forms. This intermediary step improves accuracy by providing a more reliable bridge between natural language and formal logic representations.
Solution Approach 2:
The patent segments the semantic parsing process into distinct stages: generating canonical forms from natural language utterances, and then mapping canonical forms to logical forms. This segmentation allows each stage to be optimized independently, with the canonical form generation focusing on capturing semantic structure and the logical form mapping focusing on knowledge base integration, thereby improving overall accuracy.
2Adaptability or versatility
If a small generic grammar is used to generate canonical forms and pair them with logical forms, then the system can be quickly developed for new knowledge bases and domains, but the performance is poor with accuracy of less than 50%
Solution Approach 1:
The patent employs neural network models that are trained on domain-specific data to automatically learn and generate canonical forms without requiring manual grammar construction. The system self-adapts to new domains through training on domain-specific corpora, automatically capturing domain-specific linguistic patterns and semantic structures, thereby maintaining high accuracy while being easily adaptable to new domains.
Solution Approach 2:
The patent changes the approach from using fixed generic grammar rules to using learned parameters from neural networks. The neural networks learn domain-specific parameters during training, allowing the system to adapt to different domains by simply retraining with domain-specific data rather than manually adjusting grammar rules, thus maintaining both adaptability and high accuracy.
Data Source
AI summary
A method for predicting a canonical form for an input text sequence includes predicting the canonical form with a neural network model. The model includes an encoder, which generates a first representation of the input text sequence based on a representation of n-grams in the text sequence and a second representation of the input text sequence generated by a first neural network. The model also includes a decoder which sequentially predicts terms of the canonical form based on the first and second representations and a predicted prefix of the canonical form. The canonical form can be used, for example, to query a knowledge base or to generate a next utterance in a discourse.