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Empathy and Persona Based Arabic Conversational Agent
(Saudi Digital Library) ABDULKARIM EMAD MOHAMMAD ALABDULKARIM; Yoshie Osamu
Arabic NLP has seen significant advances in Natural Language Understanding (NLU) with language models such as AraBERT and ArBert. Despite that, the Arabic Natural Language Generation (NLG) remains a challenge. Due to the lack of Arabic datasets suitable to train NLG models and the lack of pre-trained Arabic models for NLG. Therefore, we plan to pave the way for future Arabic chatbot research and Arabic NLG. By fine-tuning an empathetic and personified, Arabic conversational agent. By using “persona” we make the conversation more engaging. And with “empathy” we make the conversation more human-like. Currently, the state of Arabic chatbot research is led by the paper “Empathetic BERT2BERT Conversational Model: Learning Arabic Language Generation with Little Data.” which has the empathy part but it’s still lacking on the chit-chat model part. And so, we have managed to integrated persona into our final model by integrating and fine-tuning the model, which is a BERT2BERT architecture, on the ArabicPersonaChat dataset. Scoring 0.79 on the BLEU benchmark on the “ArabicEmpatheticDialogues” dataset compared to the previous SOTA which scored 0.675.
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