Multimodal Dialog State Circulation via Protocol Buffers
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Solution Overview
Problem
Conventional mobile devices struggle with efficiently handling multimodal inputs, such as voice and non-verbal responses, in dialog systems, leading to increased latency and data transmission issues, especially when maintaining state consistency across different network connections.
Innovation Solution
Implementing a method where user devices receive and manage multiple protocol buffers (DialogTurnIntents) to handle voice and non-verbal inputs locally, updating the dialog state and interpreting voice inputs while reducing data transmission by bundling state information and sending it to the dialog system only when necessary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the mobile device sends all dialog state information to the server system for processing, then the dialog system can maintain state consistency, but data transmission volume increases and latency increases
Solution Approach 1:
The dialog state information is segmented into two parts: critical state information that must be synchronized with the server, and local state information that can be processed and cached on the mobile device. This segmentation allows the device to operate with reduced latency while maintaining necessary state consistency through selective synchronization.
Solution Approach 2:
The mobile device is empowered with local processing capabilities to handle and interpret dialog state information without requiring constant server communication. The device maintains a local copy of dialog state and can process non-verbal responses locally, reducing data transmission needs while preserving state consistency for critical operations.
2Reliability
If the mobile device sends all dialog state information to the server system, then state consistency is maintained, but data transmission volume increases
Solution Approach 1:
Essential state information is extracted and retained on the mobile device, while only necessary updates and critical state changes are transmitted to the server. This extraction approach reduces data transmission volume significantly while maintaining the state consistency required for proper dialog management.
Solution Approach 2:
The mobile device performs preliminary processing of dialog state information locally, preparing and filtering data before transmission to the server. This preliminary action reduces the volume of data that needs to be transmitted while ensuring that the server receives only the most relevant state information for maintaining consistency.
3Reliability
If the dialog system processes all inputs centrally on the server, then state consistency is ensured, but network dependency increases and private data must be transmitted
Solution Approach 1:
Processing responsibilities are segmented between the mobile device and server system. The device handles local processing of non-verbal responses and maintains local state, while the server handles critical dialog management. This segmentation reduces network dependency while preserving necessary state consistency through selective synchronization.
Solution Approach 2:
The mobile device is equipped with self-service capabilities to process and interpret dialog state information locally without requiring constant server intervention. The device can independently handle non-verbal responses and maintain local state consistency, reducing network dependency while ensuring critical state information is synchronized with the server.
4Loss of time
If the device handles multimodal inputs locally with multiple protocol buffers, then latency is reduced and data transmission is minimized, but device complexity increases
Solution Approach 1:
The protocol buffer framework provides a universal, standardized mechanism for handling multiple types of dialog state information and non-verbal responses. This multi-functional approach allows the device to manage complex multimodal inputs through a unified interface, reducing the apparent complexity despite the sophisticated processing capabilities required.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for managing dialogs. In one aspect, a method includes receiving a request to perform a task from a user device; obtaining a dialog corresponding to the task; providing multiple protocol buffers to the user device; receiving a voice input and one or more annotated protocol buffers from the user device, the one or more annotated protocol buffers identifying corresponding non-verbal responses to content in the protocol buffers; and using the received protocol buffers to update a state of the dialog and to interpret the voice input.


