Generating Questions Based on Semi-Automated and End-to-End Neural Network

With the emergence of large-scale knowledge base, how to use triple information to generate natural questions is a key technology in question answering systems. The traditional way of generating questions require a lot of manual intervention and produce lots of noise. To solve these problems, we propose a joint model based on semi-automated model and End-to-End neural network to automatically generate questions. The semi-automated model can generate question templates and real questions combining the knowledge base and center graph. The End-to-End neural network directly sends the knowledge base and real questions to BiLSTM network. Meanwhile, the attention mechanism is utilized in the decoding layer, which makes the triples and generated questions more relevant. Finally, the experimental results on SimpleQuestions demonstrate the effectiveness of the proposed approach.     

An investigation of the drivers,barriers, and incentives for environmental management systems in the Malaysian food and beverage industry

Clean Technologies and Environmental Policy - Food production and consumption is one of the major causes of global environmental degradation. One way to address environmental impacts in the food...     

A numerical study on the soot and combustion performance of a diesel engine with pip shape

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