Discourse Phrase Discovery for Digital Assistant Language Models
Solution Overview
Problem
Conventional digital assistants face challenges in accurately understanding the context of received commands, particularly when they are vague or undefined, and struggle to execute actions across a wide range of mobile applications, due to limitations in natural language processing and the need for user-specific training.
Innovation Solution
The development of improved command interpretation techniques using principles of discourse communities, semiotics, and intertextuality to generate sophisticated language models that can automatically understand the intention behind commands, allowing for accurate selection of appropriate actions on digital assistant devices without extensive user intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional natural language processing algorithms are used to interpret commands, then the system is simple to implement, but the accuracy of understanding vague or undefined commands is poor
Solution Approach 1:
The language model is segmented into multiple components including discourse phrase discovery module, semantic role labeling module, and intent classification module. Each component processes specific aspects of command interpretation independently, improving overall accuracy while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The system transitions from traditional single-dimension keyword matching to multi-dimensional analysis by incorporating discourse phrases, semantic roles, and contextual relationships. This dimensional expansion enables accurate interpretation of vague commands by analyzing them from multiple linguistic perspectives simultaneously.
2Measurement precision
If user-specific training is conducted to improve command interpretation, then the accuracy for that user improves, but the time and resources required for training increase
Solution Approach 1:
The language model performs self-training by automatically discovering discourse phrases and semantic patterns from existing command data without requiring manual user training. The system self-optimizes its interpretation capabilities through automated discourse phrase discovery and semantic role learning, eliminating the need for time-consuming user-specific training sessions.
Solution Approach 2:
The system performs preliminary discourse phrase discovery and semantic pattern recognition during the model generation phase, preparing the language model in advance to handle diverse user commands. This preliminary action pre-configures the model with broad linguistic patterns, reducing the need for subsequent user-specific adaptation and training time.
3Measurement precision
If the language model is made more sophisticated to handle vague commands, then the interpretation accuracy improves, but the computational resources required increase
Solution Approach 1:
The system applies sophisticated discourse phrase analysis and semantic role labeling only to portions of commands that are ambiguous or vague, rather than processing every command with full complexity. This localized application of advanced processing reduces overall computational energy consumption while maintaining high accuracy for difficult-to-interpret commands.
Solution Approach 2:
The language model performs partial discourse phrase discovery and semantic analysis based on the complexity and ambiguity of each input command. For clear, unambiguous commands, the system performs minimal processing, while allocating more computational resources only when needed for vague or undefined commands, optimizing the balance between accuracy and energy consumption.
Data Source
AI summary
Disclosed are techniques for the automated discovery and extraction of discourse phrases, or in other words terms that representative of a topic or concept communicated via a plurality of electronic documents, to facilitate the generation of a language model that is applicable to interpreting commands for invoking application-based actions via a digital assistant device. An electronic document corpus can be generated having a plurality of electronic documents that are clustered based on entropy, among other things. The clusters can be associated with a corresponding plurality of cluster attractors that are generally representative of a context of the documents included therein. The clusters can be grouped based on determined relationships, such that the groups can be further analyzed and terms or phrases representative of a discourse associated with the grouped documents can be determined. The various groups and clusters of documents can be saved and indexed, among other things, to facilitate more efficient searching of the electronic document corpus to interpret received inputs, such as commands received via a digital assistant device.