![]() ![]() ![]() Serban I, Sankar C, Germain M, Zhang S, Lin Z, Subramanian S, Kim T, Pieper M, Chandar S, Ke N, Mudumba S, Brebisson A, Sotelo J, Suhubdy D, Michalski V, Nguyen A, Pineau J, Bengio Y (2017) A Deep Reinforcement Learning Chatbot Kassel, Germany: International Journal of Emerging Technology in Learning ![]() International journal of emerging Technologies in Learning (iJET), 14(24), 56–68. Palasundram K, Mohd Sharef N, Nasharuddin N, Kasmiran K, Azman A (2019) Sequence to sequence model performance for education Chatbot. Nguyen H, Morales D (2017) A neural Chatbot with personality Mozannar H, Hajal K, Maamary, E, Hajj H (2019) Neural Arabic Question Answering Microsoft (2017b) Microsoft bot framework. Hadj Ameur M, Meziane F, Guessoum A (2017) Arabic machine transliteration using an attention-based encoder-decoder model. Goda Y, Yamada M, Matsukawa H, Hata K, Yasunami S (2014) Conversation with a chatbot before an online EFL group discussion and the effects on critical thinking. įouad M, Mahany A, Katib I (2020) Masdar: a novel sequence-to-sequence deep learning model for Arabic stemming. ![]() įadhil A, AbuRa'ed A (2019) OlloBot -towards a text-based arabic health conversational agent: evaluation and results. hULMonA (حلمنا): The Universal Language Model in Arabic. Ĭho K, van Merrienboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, Bengio Y (2014) Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine TranslationĮljundi O, Antoun W, El Droubi N, Hajj H, El-Hajj W, Shaban K (2019). Īntoun W, Baly F, Hajj H (2020) AraBERT: Transformer-based Model for Arabic Language UnderstandingĬho K, van Merriënboer B, Gulcehre C, Bougares F, Schwenk H, Bengio Y (2014) Learning phrase representations using RNN encoder-decoder for statistical machine translation. In: Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: System Demonstrations, 2016, pp 208–212Īlotaiby F, Foda S, Alkharashi I (2012) New approaches to automatic headline generation for Arabic documents. Īli DA, Habash N (2016) Botta: An arabic dialect chatbot. Īl-Humoud S, Aldamegh W (2018) Arabic chatbots: a survey. The results obtained are significant, In most questions the chatbot was able to reproduce good answers.Īl-Ghadhban D, Al-Twairesh N (2020) Nabiha: an Arabic dialect chatbot. Our algorithm was trained on a VM on google cloud (GPU TESLA K80 10 GO). We use a dataset of ~81,659 pairs of conversations created manually and without any handcrafted rules. We built the model and tested it in the Tensorflow 2 deep learning framework using the most seq 2 seq Model architectures. midoBot is capable of conversing with humans on popular conversation topics through text. In this paper, we present midoBot: a deep learning Arabic chatbot based on the seq2seq model. Although chatbots can be used for a variety of tasks, they generally need to understand what users are saying and to provide appropriate answers to their questions. Indeed, since the birth of AI, creating a good chatbot remains one of the most difficult challenges in this field. Conversation modeling is an extremely important topic in natural language processing and artificial intelligence (AI). A conversational agent (chatbot) is a software that can communicate with humans using natural language. ![]()
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