Few-shot Named Entity Recognition via Superposition Concept Discrimination

Core Concepts

SuperCD effectively improves few-shot NER performance by identifying and discriminating superposition concepts, resolving precise generalization challenges.

Abstract

Few-shot NER aims to identify entities with limited instances.
SuperCD introduces active learning to identify superposition concepts.
Concept Extractor and Superposition Instance Retriever are key components.
Experiments show significant performance improvements across datasets.
SuperCD is effective under different annotation budgets.

Stats

"Experiments show that SuperCD significantly outperforms baselines."
"SuperCD improves the performance by 11.4% compared to vanilla models."
"Compared with random sampling baselines, SuperCD improves the performance by 3.3%."

Quotes

"SuperCD can effectively improve the performance of FS-NER by retrieving high-value instances."
"Discriminating superposition concepts is helpful to resolve precise generalization challenge."

Key Insights Distilled From

Few-shot Named Entity Recognition via Superposition Concept Discrimination

by Jiawei Chen,... at arxiv.org 03-26-2024

https://arxiv.org/pdf/2403.16463.pdf

Deeper Inquiries

How can SuperCD be adapted for other few-shot tasks?

SuperCD can be adapted for other few-shot tasks by following a similar active learning paradigm. The key is to identify critical superposition concepts specific to the task at hand and provide additional instances of these concepts for supervision. This involves training a Concept Extractor to generalize illustrative instances into common concepts and then constructing sets of superposition concepts based on an "A but not B" manner. A Superposition Instance Retriever is used to retrieve instances corresponding to these superposition concepts from a large-scale corpus, which are then annotated by human annotators. These annotated instances, along with the initial illustrative data, are used to train models for the specific few-shot task.

What potential biases or limitations could arise from human annotators in the active learning process?

Annotation Bias: Human annotators may introduce bias when selecting or labeling instances, leading to skewed datasets that do not accurately represent the target concept.
Inconsistency: Different annotators may interpret superposition concepts differently, resulting in inconsistent annotations that affect model performance.
Limited Domain Knowledge: Annotators with limited domain knowledge may struggle to correctly identify relevant instances of superposition concepts, impacting the quality of annotations.
Annotation Errors: Human error during annotation can introduce noise into the dataset, affecting model training and generalization.

How might the concept coverage of the Concept Extractor impact the overall performance of SuperCD?

The concept coverage of the Concept Extractor plays a crucial role in determining how well SuperCD performs in identifying relevant superposition concepts. If the Concept Extractor has limited coverage and fails to extract all common and superposition concepts accurately from illustrative instances, it can lead to:

Missing Critical Concepts: Failure to extract important common or superposition concepts could result in incomplete sets being constructed, leading to inaccurate discrimination and underperformance.
Incorrect Generalization: Inaccurate extraction of universal concepts may lead to incorrect generalizations during instance retrieval using SIR.
Reduced Discrimination Ability: Limited concept coverage hinders SuperCD's ability to discriminate between different types effectively based on extracted features.

Improving concept coverage through better training data or enhancing model capabilities can enhance SuperCD's effectiveness in identifying critical information for few-shot tasks successfully.