Neural text generation metamorphosed into several critical natural language applications ranging from text completion to free form narrative generation. Generating natural language has fundamentally been a human attribute and the advent of ubiquitous NLP applications and virtual agents marks the need to impart this skill to machines. There has been a colossal research effort in various frontiers of neural text generationincluding machine translation, summarization, image captioning, storytelling etc., We believe that this is an excellent juncture to retrospect on the directions of the field. Specifically, this paper surveys the fundamental factors and components relaying task agnostic impacts across various generation tasks such as storytelling, summarization, translation etc., In specific , we present an abstraction of the imperative techniques with respect to learning paradigms, pretraining, modeling approaches, decoding and the key challenges  . Thereby, we hope to deliver a one-stop destination for researchers in the field to facilitate a perspective on where to situate their work and how it impacts other closely related  tasks.
Neural text generation metamorphosed into several critical natural language applications ranging from text completion to free form narrative generation. In order to progress research in text generation, it is critical to absorb the existing research works and position ourselves in this massively growing field. Specifically, this paper surveys the fundamental components of modeling approaches relaying task agnostic impacts across various generation tasks such as storytelling, summarization, translation etc., In this context , we present an abstraction of the imperative techniques with respect to learning paradigms, pretraining, modeling approaches, decoding and the key challenges outstanding in the field in each of them . Thereby, we deliver a one-stop destination for researchers in the field to facilitate a perspective on where to situate their work and how it impacts other closely related generation tasks.
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Generating natural language has fundamentally been a human attribute and the advent of ubiquitous NLP applications and virtual agents marks the need to impart this skill to machines. There has been a colossal research effort in various frontiers of neural text generationincluding machine translation, summarization, image captioning, storytelling etc., We believe that this is an excellent juncture to retrospect on the directions of the
In order to progress research in text generation, it is critical to absorb the existing research works and position ourselves in this massively growing
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factors and components
components of modeling approaches
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specific
this context
A
outstanding in the field in each of them
D
hope to
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generation