Command Parsing Rule Generation via Log Analysis
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Solution Overview
Problem
Speech recognition systems face challenges in processing diverse user command inputs, as they cannot anticipate all possible commands, leading to underserved command inputs that do not parse correctly and require extensive rule generation and maintenance.
Innovation Solution
The system identifies underserved command inputs through log data analysis, using crowdsourcing techniques to determine if new parsing rules are needed, thereby reducing the costs and time associated with updating parse models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the system generates parsing rules for all possible commands, then command recognition accuracy is improved, but the complexity and maintenance cost of the parsing rule system increases significantly
Solution Approach 1:
The system uses its own operational logs to automatically identify underserved commands and generate new parsing rules. The action analysis process autonomously analyzes command input logs, determines which commands are underserved, and generates appropriate parsing rules without requiring external manual intervention or detailed studies, enabling the system to self-improve its command recognition capabilities
Solution Approach 2:
The system implements a feedback loop where command input logs from actual usage are analyzed to identify parsing failures and underserved commands. This feedback information is then used to generate new parsing rules that are added to the parsing rule set, continuously improving the system's ability to recognize diverse commands based on real-world usage patterns
2Reliability
If the system manually creates parsing rules through detailed studies, then parsing rule accuracy is improved, but the time and cost for model maintenance increases
Solution Approach 1:
The system automatically analyzes its own operational logs to identify underserved commands and generates parsing rules autonomously. The action analysis process examines command input logs, determines which commands lack adequate parsing rules, and creates new rules without requiring manual intervention from developers or researchers, significantly reducing model maintenance time and cost
Solution Approach 2:
The system performs preliminary analysis of command input logs to identify underserved commands before they cause significant issues. By continuously monitoring and analyzing usage patterns, the system proactively generates parsing rules for commands that are emerging or becoming important, preventing parsing failures before they impact user experience
Data Source
AI summary
A language processing system identifies first command input sentences that do not successfully parse by any parsing rule in a set of parsing rules. Each of the parsing rules is associated with an action, and a user device performs the action associated with a parsing rule in response to an input sentence being successfully parsed by the parsing rule. For each of these identified first sentences, the system determines whether the first input sentence has an underserving signal that is indicative of one or more actions being underserved. If the first sentence has the underserving signal, then the first sentence is selected as a candidate input sentence. Each candidate input sentence is provided to an action analysis processes that determines whether a candidate input sentence is to be associated with one action, and upon a positive determination generates a parsing rule for the candidate input sentence.