Published: July 11, 2023
Chatbots are computer programs that use artificial intelligence to imitate human conversations. Recent advancements in deep learning have shown interest utilizing language transformers, which do not rely on predefined rules and responses like traditional chatbots. This study provides a comprehensive review of previous research chatbots employ transfer models. Specifically, it examines the current trends using transformers with techniques evaluate ability Arabic understand conversation context demonstrate natural behavior. The proposed methods explore AraBERT, CAMeLBERT, AraElectra-SQuAD, AraElectra (Generator/Discriminator) different variants these semantic embedding Two datasets were used for evaluation: one 398 questions corresponding documents, another 1395 365,568 documents sourced from Wikipedia. Extensive experimental works conducted, evaluating both manually crafted entire set questions, confidence similarity metrics. results showed AraElectra-SQuAD model achieved an average score 0.6422 0.9773 first dataset, 0.6658 0.9660 second dataset. concludes consistently outperformed other models, displaying remarkable performance, high confidence, scores, as well robustness, highlighting its potential practical applications processing tasks suggests can be further enhanced applied various such chatbots, virtual assistants, information retrieval systems Arabic-speaking users. By combining power transformer architecture fine-tuning SQuAD-like large data, this trend demonstrates provide accurate contextually relevant answers Arabic.
Language: Английский