Large-Scale Label Interpretation Learning for Few-Shot Named Entity Recognition

Core Concepts

Scaling up entity types in label interpretation learning significantly improves few-shot NER performance.

Abstract

Abstract:

Few-shot NER detects named entities with minimal examples.
Leveraging natural language descriptions of entity types enhances model performance.

Introduction:

Transfer learning with pre-trained models is common in few-shot NER.
Two-phase learning process involves label interpretation and tagset extension.

Limitations:

Common datasets limit semantic signal during label interpretation learning.

Contributions:

LITSET approach scales up entity types for improved few-shot NER.

Validation Experiment:

Increasing distinct entity types and expressive descriptions boosts few-shot performance.

Large-Scale Label Interpretation Learning:

Dataset derived from ZELDA and WikiData annotations enhances label interpretation training.

Backbone Architecture:

Bi-encoder model simplifies experiments but may require adaptations for more labels.

Experiments:

In-domain, cross-domain, cross-lingual transfers show significant improvements with LITSET.

Related Work:

Comparison to existing methods like metric learning and template-based approaches.

Stats

Few-shot NER employs transfer learning with pre-trained models (PLMs).
Common datasets like CoNLL-03 have limited distinct entity types for label interpretation learning.

Quotes

"Few-shot performance increases with label interpretation learning on more distinct entity types and more expressive descriptions."
"We find that increasing the expressiveness of label verbalizations strongly improves the few-shot performance."

Key Insights Distilled From

Large-Scale Label Interpretation Learning for Few-Shot Named Entity Recognition

by Jonas Golde,... at arxiv.org 03-22-2024

https://arxiv.org/pdf/2403.14222.pdf

Deeper Inquiries

How can biases in entity linking datasets be mitigated to prevent bias in model training?

Biases in entity linking datasets can be mitigated through several strategies:

Diverse Data Collection: Ensuring that the dataset includes a wide range of sources and perspectives can help reduce bias by providing a more comprehensive view of entities.
Annotation Guidelines: Clear and unbiased annotation guidelines should be established to ensure consistency and fairness in labeling entities.
Bias Detection Algorithms: Implementing algorithms that detect and flag biased annotations or labels can help researchers identify and rectify potential biases before training models.
Regular Auditing: Regularly auditing the dataset for biases, especially during the data collection phase, can help maintain data integrity.
Balanced Sampling: Ensuring balanced representation of different groups or categories within the dataset can prevent over-representation or under-representation biases.

How are inconsistent annotations likely to impact model performance in few-shot scenarios?

Inconsistent annotations can have significant implications on model performance in few-shot scenarios:

Model Instability: Inconsistencies may lead to confusion for the model during training, resulting in unstable learning patterns that hinder its ability to generalize effectively.
Reduced Generalization Ability: Models trained on inconsistently annotated data may struggle to adapt well to new tasks or unseen labels due to conflicting information learned during training.
Increased Error Rates: Inconsistencies could introduce noise into the training process, leading to higher error rates when making predictions on new instances with limited labeled examples.

How could contrastive loss or other loss functions be integrated into the LITSET approach to enhance adaptability?

Integrating contrastive loss or other loss functions into the LITSET approach could enhance adaptability by:

Improved Feature Learning - Contrastive loss encourages models to learn representations where similar inputs are closer together while dissimilar inputs are pushed apart, aiding in better feature extraction for entity types with varying characteristics.
Enhanced Discriminative Power - By incorporating contrastive loss, LITSET could improve its ability to discriminate between different entity types even with limited labeled examples, leading to better generalization across diverse classes.
Robustness Against Noisy Annotations - Contrastive loss is known for being robust against noisy labels; integrating it into LITSET could help mitigate issues arising from inconsistent annotations present in large-scale datasets like ZELDA.

By leveraging these advanced loss functions alongside existing methodologies within LITSET's label interpretation learning framework, researchers may achieve more robust few-shot NER models capable of adapting efficiently across various domains and languages while minimizing errors caused by inconsistencies in annotations."