Coarse Fine Semantic Parser for Dialog Accuracy
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Solution Overview
Problem
Conventional semantic parsers in dialog systems face challenges in accurately mapping complex utterances to logical forms due to data sparsity, leading to inaccurate inputs for the dialog manager subsystem.
Innovation Solution
The implementation of an improved semantic parser comprising a coarse semantic parser and a fine semantic parser in series, where the coarse parser maps an utterance to an intermediate logical form, and the fine parser maps the utterance and intermediate logical form to a final logical form.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single semantic parser is used to map utterances to logical forms, then the system structure is simple, but the data sparsity problem leads to inaccurate mapping
Solution Approach 1:
The patent divides the single semantic parser into two separate parsers: a coarse semantic parser that maps utterances to intermediate logical forms, and a fine semantic parser that maps intermediate logical forms to final logical forms. This segmentation allows each parser to specialize in a specific mapping task, improving overall mapping accuracy while addressing the data sparsity problem through divided learning responsibilities.
2Reliability
If the task of determining logical form is divided into two smaller tasks, then the data sparsity problem is reduced, but the system complexity increases
Solution Approach 1:
The learning task is segmented into two distinct phases: the coarse parser learns to map utterances to intermediate logical forms using a first set of training tuples, while the fine parser learns to map intermediate logical forms to final logical forms using a second set of training tuples. This segmentation reduces the complexity of the learning problem for each individual parser, improving reliability.
Solution Approach 2:
The intermediate logical form serves as an intermediary representation between the utterance and the final logical form. This intermediary structure allows the system to break down the complex mapping task into manageable steps, where each parser can learn from specialized training data, thereby improving learning effectiveness without requiring the entire system to handle the full complexity at once.
3Measurement precision
If a coarse parser and fine parser are used in series, then the output distribution becomes more manageable, but the processing time increases
Solution Approach 1:
The processing pipeline is segmented into two sequential stages: the coarse parser first processes the utterance to generate an intermediate logical form, then the fine parser processes the intermediate form to generate the final logical form. This segmentation allows each parser to focus on a specific aspect of the mapping, improving output distribution accuracy by reducing the complexity of what each individual parser must learn.
Data Source
AI summary
Techniques for improving a semantic parser of a dialog system, by breaking the semantic parser into a coarse semantic parser and a fine semantic parser, are described. A method described herein includes accessing an utterance received in a dialog system. The utterance is a text-based natural language expression. The method further includes applying a coarse semantic parser to the utterance to determine an intermediate logical form for the utterance. The intermediate logical form indicates one or more intents in the utterance. The method further includes applying a fine semantic parser to the intermediate logical form to determine a logical form for the utterance. The logical form is a syntactic expression of the utterance according to an established grammar, and the logical form includes one or more parameters of the one or more intents. The logical form can be used to conduct a dialog with a user of the dialog system.