Pawan Lahoti
ABSTRACT
Multilingual Question Answering (MQA) generates accurate answers to the user’s query despite the context language. MQA has gained popularity as individuals increasingly pose questions in both English and their native languages on social media. However, conventional Question Answering (QA) systems encounter difficulties in handling multiple languages. The development of MQA models is hindered by the lack of large-scale benchmark datasets, impeding the achievement of high performance compared to monolingual systems. An effective MQA system called OptBertDCNN is proposed to address this, leveraging multilingual cased Bidirectional Encoder Representations from Transformers (mBERT) and Optimized Deep Convolutional Neural Networks (CNN). OptBertDCNN employs a BERT tokenizer to segment sentences into tokens, enabling the extraction of features such as word embeddings from the pre-trained model, TF-IDF scores, SentiWordNet scores, and statistical features from both context and question tokens. These informative features are then inputted into OptBertDCNN, which is trained using the EHMQuAD dataset. Notably, OptBertDCNN achieves outstanding performance metrics, including an exact match of 0.755, precision of 0.765, recall of 0.773, and F1-score of 0.769. These results unequivocally demonstrate the effectiveness of OptBertDCNN in addressing the challenges of multilingual question answering.
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Index Terms
- OptBertDCNN: A framework based on BERT and optimized Deep Convolutional Neural Network for MQA
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