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Explore automated machine learning (AutoML) techniques for natural language processing in this comprehensive EACL'13 tutorial. Delve into the emerging field of AutoML and its potential impact on NLP model building. Learn about hyperparameter optimization, neural architecture search, and other key topics that have gained prominence at major conferences like NeurIPS, ICML, and ICLR. Discover how automation can introduce rigor to the often ad hoc process of model development, moving beyond borrowing default hyperparameters and trying variant architectures. Gain insights into applying AutoML techniques to enhance the NLP model-building process, ensuring more optimal and rigorous results. This 2-hour and 44-minute tutorial, presented by Kevin Duh and Xuan Zhang from the Center for Language & Speech Processing (CLSP) at Johns Hopkins University, offers a thorough overview of AutoML methods and their practical applications in the field of natural language processing.
