Question Classification with Deep Contextualized Transformer

TL;DR

This paper introduces a novel deep contextualized transformer approach for question classification, demonstrating significant improvements in accuracy over existing methods through extensive evaluations on benchmark datasets.

Contribution

The paper presents a new transformer-based method for question classification that outperforms previous approaches in accuracy and efficiency.

Findings

Significant accuracy improvements on SQuAD and SwDA datasets

Effective handling of aberrant expressions in questions

Enhanced model efficiency for QA classification

Abstract

The latest work for Question and Answer problems is to use the Stanford Parse Tree. We build on prior work and develop a new method to handle the Question and Answer problem with the Deep Contextualized Transformer to manage some aberrant expressions. We also conduct extensive evaluations of the SQuAD and SwDA dataset and show significant improvement over QA problem classification of industry needs. We also investigate the impact of different models for the accuracy and efficiency of the problem answers. It shows that our new method is more effective for solving QA problems with higher accuracy