BERTer: The Efficient One

TL;DR

This paper introduces BERTer, an enhanced fine-tuning framework for BERT that combines regularization, architecture innovations, and early exiting to significantly improve performance and efficiency across various NLP tasks.

Contribution

It presents novel fine-tuning techniques including SMART regularization, a cross-embedding Siamese architecture, and early exiting methods to advance BERT's adaptability and performance.

Findings

Achieved state-of-the-art results on multiple NLP benchmarks.

Demonstrated substantial improvements in model efficiency and effectiveness.

Showcased the benefits of combining multiple fine-tuning architectures.

Abstract

We explore advanced fine-tuning techniques to boost BERT's performance in sentiment analysis, paraphrase detection, and semantic textual similarity. Our approach leverages SMART regularization to combat overfitting, improves hyperparameter choices, employs a cross-embedding Siamese architecture for improved sentence embeddings, and introduces innovative early exiting methods. Our fine-tuning findings currently reveal substantial improvements in model efficiency and effectiveness when combining multiple fine-tuning architectures, achieving a state-of-the-art performance score of on the test set, surpassing current benchmarks and highlighting BERT's adaptability in multifaceted linguistic tasks.