Transformer-Based Models for Efficient Natural Language Processing

Introduction

Transformer-based models have become the dominant architecture in natural language processing, enabling significant advances in various tasks such as machine translation, text summarization, and question answering.

Problem Statement

Despite their impressive performance, transformer models face scalability challenges due to their quadratic complexity with respect to sequence length, limiting their applicability to long documents.

Proposed Solution

Our hybrid architecture introduces recurrent components that work in conjunction with the attention mechanism, allowing for efficient processing of long sequences while maintaining the modeling power of transformers.

Experimental Results

We evaluate our approach on standard NLP benchmarks, including GLUE and SQuAD, demonstrating competitive performance with significant efficiency gains.

Conclusion

The proposed hybrid architecture offers a promising direction for developing efficient and effective language models for real-world applications.