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. This is a challenging problem as it spans a wide range of topic and contextual domains. Moreover, such language processing must deal with slang, foreign words and passages, localised dialects, unpredictably
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development process for DL, covering requirement analysis, data collection and labeling, data cleaning, network design, training, testing, and operation. Required knowledge deep learning, natural language
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experimental hydrodynamics, naval nuclear propulsion, systems engineering, energy technologies, fluid processes in maritime and nuclear or relevant industry applications. Record of research achievements as
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Learning. Conference on Empirical Methods in Natural Language Processing (EMNLP'20). Hua, Yuncheng; Qi, Daiqing; Zhang, Jingyao; Qi, Guilin; Li, Yuan-Fang. Less is More: Data-efficient Complex Question