Reparametrized RNN for Email Action Identification
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Solution Overview
Problem
Current automated email systems lack intelligence in responding to emails, as they rely on minimal filters and do not tailor replies based on the email content, and existing public email datasets are insufficient for training machine learning systems due to lack of annotation and small size.
Innovation Solution
A method using a reparametrized recurrent neural network model trained on annotated email datasets and unannotated conversation data from similar structures, applying an action-based annotation scheme to predict recipient actions, and utilizing multitask and multidomain learning to improve performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional email filtering systems are used, then the system is simple to implement, but the system lacks intelligence and cannot accurately identify recipient actions
Solution Approach 1:
The patent introduces a reparametrized recurrent neural network model as an intermediary between email input and action identification output. This neural network model serves as a mediator that processes email content through learned representations to accurately predict recipient actions, resolving the contradiction by providing intelligent processing without requiring complex manual rule systems
Solution Approach 2:
The patent applies reparameterization to the recurrent neural network model, transforming the model parameters to improve action identification accuracy. By changing the parameter representation and optimization approach, the system achieves better performance while maintaining a manageable system architecture
2Reliability
If public email datasets are used for training, then data availability is high, but the datasets lack annotation and are insufficient for training machine learning systems
Solution Approach 1:
The patent performs preliminary action by pre-annotating email datasets with recipient actions before training the neural network model. This advance preparation of high-quality labeled data ensures that the model receives sufficient training information, resolving the contradiction between data quality and quantity requirements
Solution Approach 2:
The patent develops a universal action-based annotation scheme that can be applied across different email datasets and domains. This multi-functional annotation approach allows the same labeling framework to work with various data sources, effectively increasing the usable training data volume while maintaining consistent quality standards
3Adaptability or versatility
If the neural network is trained only on annotated email data, then training accuracy is high, but the system lacks generalization to different communication domains
Solution Approach 1:
The patent implements multidomain learning by training the neural network on multiple communication domains (emails, chats, meetings) simultaneously. The model learns domain-invariant features that generalize across different communication types while maintaining task-specific performance, resolving the contradiction between adaptability and precision
Solution Approach 2:
The patent segments the training process into domain-specific preprocessing and unified model training. Each domain's data is prepared with domain-appropriate features, then all segments are combined for joint training, allowing the model to learn both domain-specific nuances and universal patterns
4Ease of operation
If minimal filters are used in automated email systems, then the system is fast and simple, but the system cannot tailor replies based on email content
Solution Approach 1:
The patent replaces mechanical filtering rules with a neural network-based semantic understanding system. The recurrent neural network processes email content to extract meaning and predict appropriate actions, substituting simple keyword filtering with intelligent content analysis that preserves email semantic information
Data Source
AI summary
A method includes receiving an email addressed to a recipient user, processing the received email using a reparametrized recurrent neural network model to identify an action based on the received email, and wherein the reparametrized recurrent neural network model has been trained on an email dataset annotated with recipient corresponding actions and reparametrized on unannotated conversation data having structures similar to email data.