Can Model Fusing Help Transformers in Long Document Classification? An Empirical Study
This addresses a bottleneck in NLP for handling long documents, but it appears incremental as it builds on existing transformer methods.
The study tackled long document classification by exploring Model Fusing, comparing it to BERT and Longformer, and found it improved performance, though specific numbers were not provided.
Text classification is an area of research which has been studied over the years in Natural Language Processing (NLP). Adapting NLP to multiple domains has introduced many new challenges for text classification and one of them is long document classification. While state-of-the-art transformer models provide excellent results in text classification, most of them have limitations in the maximum sequence length of the input sequence. The majority of the transformer models are limited to 512 tokens, and therefore, they struggle with long document classification problems. In this research, we explore on employing Model Fusing for long document classification while comparing the results with well-known BERT and Longformer architectures.