Survey of transformers and towards ensemble learning using transformers for natural language processing.

Area of Science:

• Natural Language Processing
• Deep Learning
• Artificial Intelligence

Background:

• Transformer models, introduced by Google in 2017, have revolutionized Natural Language Processing (NLP).
• Pre-trained models like BERT, XLNet, RoBERTa, and ALBERT have shown significant success across diverse NLP tasks.
• Existing research offers limited comprehensive comparisons of these advanced transformer architectures.

Purpose of the Study:

• To systematically describe and compare prominent transformer models (BERT, XLNet, RoBERTa, GPT2, ALBERT).
• To evaluate the performance of these models on a range of established NLP tasks.
• To introduce and assess ensemble learning models for enhanced NLP task performance.

Main Methods:

• Comparative analysis of BERT, XLNet, RoBERTa, GPT2, and ALBERT models.
• Application and evaluation of models on six key NLP tasks: sentiment analysis, question answering, text generation, text summarization, named entity recognition, and topic modeling.
• Development and testing of ensemble learning models integrating existing transformer architectures.

Main Results:

• Performance analysis revealed varying strengths and weaknesses of individual transformer models across different NLP tasks.
• Ensemble learning models consistently outperformed single transformer models on specific evaluated tasks.
• The study provides empirical evidence for the effectiveness of ensemble approaches in NLP.

Conclusions:

• Transformer models offer powerful capabilities for a wide array of NLP challenges.
• Ensemble methods represent a promising direction for advancing NLP task performance beyond individual model limitations.
• Further research into specialized ensemble strategies can unlock greater potential in complex NLP applications.