Dependency Parsing for Spoken Language Intent Accuracy
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Solution Overview
Problem
Conventional spoken language understanding systems struggle with complex sentences, particularly longer and more spontaneous utterances, leading to inaccurate processing and misinterpretation of intent, especially when word dependencies exceed five words.
Innovation Solution
A dependency parsing-based sentence simplification approach that extracts keywords from natural language sentences to improve accuracy, involving speech-to-text conversion, acoustic and language modeling, and syntactic information preprocessing, which rephrases sentences to focus on key intent words, handling long distance dependencies and simplifying utterances for better intent determination and slot filling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional word n-gram systems with n < 5 are used, then the system is simple and computationally efficient, but it cannot discern intent changes when the changing word is more than five words away in the original sentence
Solution Approach 1:
The patent segments sentences into dependency parse trees, breaking down the linguistic structure into hierarchical relationships between words. This segmentation allows the system to handle long-distance dependencies by focusing on local syntactic relationships rather than requiring global word sequences, thereby improving intent determination without proportionally increasing system complexity.
Solution Approach 2:
The patent introduces dependency parse trees as an intermediary representation between raw text and intent determination. This intermediary structure captures syntactic relationships and enables the system to discern intent changes even when words are far apart in the original sentence, effectively bridging the gap between simple n-gram systems and complex semantic understanding.
2Measurement precision
If simple well-formed sentences are processed, then conversion accuracy is high, but error rate increases as sentences become more complex and longer
Solution Approach 1:
The patent changes the parameter of sentence representation from raw text to dependency parse trees. This transformation allows the system to maintain high conversion accuracy for well-formed sentences while also effectively processing complex and spontaneous utterances, as the parse tree structure captures the essential syntactic relationships regardless of sentence length or complexity.
3Adaptability or versatility
If speakers use natural and spontaneous utterances, then the language is more realistic and varied, but the conversion accuracy decreases and processing becomes more difficult
Solution Approach 1:
The patent performs preliminary syntactic analysis by converting natural language utterances into dependency parse trees before intent determination. This preliminary action structures the spontaneous and varied language inputs, making them easier to process accurately and enabling the system to handle natural language effectively without sacrificing conversion precision.
Data Source
AI summary
Sentence simplification may be provided. A spoken phrase may be received and converted to a text phrase. An intent associated with the text phrase may be identified. The text phrase may then be reformatted according to the identified intent and a task may be performed according to the reformatted text phrase.