This paper introduces a novel ensemble approach for question classification using state-of-the-art models -- Electra, GloVe, and LSTM. The proposed model is trained and evaluated on the TREC dataset, a well-established benchmark for question classification tasks. The ensemble model combines the strengths of Electra, a transformer-based model for language understanding, GloVe, a global vectors for word representation, and LSTM, a recurrent neural network variant, providing a robust and efficient solution for question classification. Extensive experiments were carried out to compare the performance of the proposed ensemble approach with other cutting-edge models, such as BERT, RoBERTa, and DistilBERT. Our results demonstrate that the ensemble model outperforms these models across all evaluation metrics, achieving an accuracy of 0.8 on the test set. These findings underscore the effectiveness of the ensemble approach in enhancing the performance of question classification tasks, and invite further exploration of ensemble methods in natural language processing.

翻译：本文提出了一种新颖的集成方法，利用当前先进的模型——Electra、GloVe与LSTM进行问题分类。所提出的模型在TREC数据集（一个公认的问题分类基准测试数据集）上完成了训练与评估。该集成模型结合了基于Transformer的语言理解模型Electra、全局词向量表示模型GloVe以及循环神经网络变体LSTM的优势，为问题分类任务提供了一种稳健且高效的解决方案。通过大量实验，我们将所提出的集成方法与其他前沿模型（如BERT、RoBERTa和DistilBERT）的性能进行了比较。结果表明，该集成模型在所有评估指标上均优于这些模型，在测试集上达到了0.8的准确率。这些发现凸显了集成方法在提升问题分类任务性能方面的有效性，并为进一步探索自然语言处理中的集成方法提供了启示。