In this paper, we address the problem of Arabic community question answering. We propose a model that leverages both the archived question and answer representations in the similarity computation with the user's question. The proposed model considers the interaction of the user's question with both archived questions and answers separately to address the noisy information problem in Arabic community question answering. The proposed model is a combination of two parts that covers question-question similarity and question-answer relevance. We used twin ON-LSTM with an attention mechanism and Arabic ELMo embeddings as input for the question-question similarity. For the question-answer relevance, we used a combination of twin ON-LSTM and CNN networks which can capture the relevance score even with long answers and questions. We evaluated the proposed model on the biomedical Arabic community question answering dataset cQA-MD. The proposed model outperformed the previous studies evaluated on the same dataset.
CCS CONCEPTS• Computing methodologies → Artificial intelligence; Natural language processing.
Cloud computing becomes the basic alternative platform for the most users application in the recent years. The complexity increasing in cloud environment due to the continuous development of resources and applications needs a concentrated integrated fault tolerance approach to provide the quality of service. Focusing on reliability enhancement in an environment with dynamic changes such as cloud environment, we developed a multi-agent scheduler using Reinforcement Learning (RL) algorithm and Neural Fitted Q (NFQ) to effectively schedule the user requests. Our approach considers the queue buffer size for each resource by implementing the queue theory to design a queue model in a way that each scheduler agent has its own queue which receives the user requests from the global queue. A central learning agent responsible of learning the output of the scheduler agents and direct those scheduler agents through the feedback claimed from the previous step. The dynamicity problem in cloud environment is managed in our system by employing neural network which supports the reinforcement learning algorithm through a specified function. The numerical result demonstrated an efficiency of our proposed approach and enhanced the reliability
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