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One of the hardest problems in the area of Natural Language Processing andArtificial Intelligence is automatically generating language that is coherentand understandable to humans. Teaching machines how to converse as humans dofalls under the broad umbrella of Natural Language Generation. Recent yearshave seen unprecedented growth in the number of research articles published onthis subject in conferences and journals both by academic and industryresearchers. There have also been several workshops organized alongsidetop-tier NLP conferences dedicated specifically to this problem. All thisactivity makes it hard to clearly define the state of the field and reasonabout its future directions. In this work, we provide an overview of thisimportant and thriving area, covering traditional approaches, statisticalapproaches and also approaches that use deep neural networks. We provide acomprehensive review towards building open domain dialogue systems, animportant application of natural language generation. We find that,predominantly, the approaches for building dialogue systems use seq2seq orlanguage models architecture. Notably, we identify three important areas offurther research towards building more effective dialogue systems: 1)incorporating larger context, including conversation context and worldknowledge; 2) adding personae or personality in the NLG system; and 3)overcoming dull and generic responses that affect the quality ofsystem-produced responses. We provide pointers on how to tackle these openproblems through the use of cognitive architectures that mimic human languageunderstanding and generation capabilities.

A Survey of Natural Language Generation Techniques with a Focus on Dialogue Systems - Past, Present and Future Directions