Multi-Task Sequence Tagging with Supplemental Context Injection
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Solution Overview
Problem
Existing sequence tagging systems are labor-intensive and resource-consuming, often requiring manual labeling and may exhibit substandard performance for certain sequences, especially when lacking sufficient contextual information.
Innovation Solution
A tagging system that incorporates a transformer-based encoder neural network to map supplemented sequences, leveraging supplemental information from search systems to enhance context, and uses a post-processing neural network to improve tag accuracy, while training with monolingual and multilingual corpora to facilitate multi-task learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If supplemental information is appended to the original sequence, then tag accuracy is improved, but device complexity increases
Solution Approach 1:
The system segments the tagging task into two distinct neural network components: an encoder neural network that processes the supplemented sequence and generates hidden state information, and a post-processing neural network that transforms hidden states into tags. This segmentation allows each component to specialize in a specific function, improving overall accuracy while managing complexity through modular design.
Solution Approach 2:
The system introduces hidden state information as an intermediary representation between the input sequence and the final tags. The encoder neural network transforms the supplemented sequence into hidden state information, which then serves as the input for the post-processing neural network. This intermediary layer enables the system to effectively integrate supplemental information without directly increasing the complexity of the tagging mechanism.
2Manufacturing precision
If manual labeling is applied to all items including supplemental information, then training precision is improved, but loss of time and computing resources increases
Solution Approach 1:
The system extracts and applies labels only to the original sequence items, while supplemental information items are assigned a default label. This extraction approach eliminates the need for manual labeling of supplemental information, significantly reducing the time and computing resources required for training while maintaining sufficient training precision through the use of ground-truth labels on the original sequence.
3Adaptability or versatility
If the system is trained with multilingual corpus for zero-shot learning, then adaptability is improved, but device complexity increases
Solution Approach 1:
The encoder neural network is trained on a multilingual corpus to achieve zero-shot learning capability, enabling the system to process and tag sequences in multiple languages without requiring language-specific training. This universal training approach allows a single model to handle diverse languages, improving adaptability while avoiding the need for separate models for each language.
Data Source
AI summary
A tagging system appends supplemental information to an original sequence of items, to produce a supplemented sequence of items. The tagging system includes a transformer-based encoder neural network that maps the supplemented sequence into hidden state information. The tagging system includes a post-processing neural network that transform the hidden state information into a tagged output sequence of items. That is, each item in the tagged output sequence includes a tag that identifies its entity class or some other characteristic. The tagging system can increase the accuracy of the tags it produces by virtue of the inclusion of the supplemental information added to each original sequence. A training system trains the tagging system to perform plural tasks, which further increases the accuracy of the tags it produces. The training system may commence training of the tagging system using a pre-trained model for the encoder neural network.