Multilingual Prompt Learning for Few-Shot Event Detection
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Solution Overview
Problem
Existing few-shot event detection methods struggle with semantic confusion due to polysemy and synonym confusions, especially in few-shot scenarios where limited instances fail to cover enough trigger semantics, and are blind to the prediction trap caused by confusing triggers.
Innovation Solution
A method and apparatus for training a few-shot event detection model based on multilingual prompt learning, which includes extracting a training dataset, performing event triggering recognition using a multilingual prompt model, generating contrastive and bilingual instances for joint event characterization, and employing a two-level hierarchical prototype network and quaternary contrastive learning to improve event tag prediction and reduce semantic confusion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If few-shot event detection is performed with limited training instances, then the model can be trained quickly and with less data, but semantic confusion occurs due to insufficient trigger semantics coverage
Solution Approach 1:
The patent introduces multilingual prompts as intermediary elements that bridge the gap between limited training data and comprehensive semantic understanding. These prompts serve as mediators that guide the model to generate richer event representations even when training instances are scarce, thereby maintaining detection accuracy while preserving training efficiency
Solution Approach 2:
The patent combines multiple linguistic resources (multilingual prompts, trigger embeddings, event type embeddings) to create a composite representation framework. This composite approach allows the model to leverage diverse linguistic cues from limited data, achieving better semantic disambiguation without requiring proportional increases in training data volume
2Device complexity
If monolingual neural networks are used for event detection, then the model structure is simple and easy to implement, but semantic confusion arises from limited monolingual clues
Solution Approach 1:
The patent transitions from monolingual to multilingual dimensionality by incorporating prompts in multiple languages. This dimensional expansion allows the model to access cross-lingual semantic information without fundamentally restructuring the core neural network architecture, thus improving trigger recognition while maintaining relative simplicity
Solution Approach 2:
The multilingual prompt framework serves multiple functions simultaneously: it provides linguistic cues for trigger identification, supplies semantic context for event type disambiguation, and enables cross-lingual knowledge transfer. This multi-functionality allows a single enhanced model structure to address multiple limitations of monolingual approaches
3Measurement precision
If external knowledge is used through enriched knowledge distillation, then synonym confusion is alleviated, but the method ignores semantic clues in context that capture relationships between confused triggers and tags
Solution Approach 1:
The patent merges external knowledge (from knowledge graphs) with contextual information (from the event text) into a unified representation framework. The multilingual prompts integrate both trigger embeddings and event type embeddings, allowing the model to simultaneously leverage external knowledge and contextual semantic clues without requiring separate processing pipelines
Solution Approach 2:
The patent performs preliminary encoding of triggers and event types into semantic embeddings before the main detection process. This preliminary action prepares enriched representations that capture both external knowledge and contextual relationships in advance, reducing the computational complexity during actual event detection while maintaining high disambiguation accuracy
Data Source
AI summary
A method and apparatus for training a few-shot event detection model based on multilingual prompt learning are provided, which includes: acquiring a training data set, applying a multilingual prompt model to any instance to obtain a predicted probability distribution of a trigger tag, so as to obtain a first loss; generating a contrastive instance and a bilingual instance, and performing multilingual prompt and cross-lingual encoding according to the input instance and the bilingual instance by applying the multilingual prompt model to obtain joint event characterization; performing event tag prediction on the joint event characterization by applying a two-level hierarchical prototype network model, and calculating a second loss; performing contrastive learning on respective instances by applying a quaternary contrastive learning module to obtain a third loss; determining a total loss of the few-shot event detection model according to respective losses, and performing model training optimization based on the total loss.