Titlebook: Deep Learning Approaches to Text Production; Shashi Narayan,Claire Gardent Book 2020 Springer Nature Switzerland AG 2020

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Design and Use of Assistive Technologyned with (i.e., text production from data, from text, and from meaning representations) and we summarise the content of each chapter. We also indicate what is not covered and introduce some notational conventions.

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https://doi.org/10.1007/978-3-540-74111-4pipeline of modules, each performing a specific subtask. The neural approach is very different from the pre-neural approach in that it provides a uniform (end-to-end) framework for text production. First the input is projected on a continuous representation (representation learning), and then, the g

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Synthesis Lectures on Human Language Technologieshttp://image.papertrans.cn/d/image/264571.jpg

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Pre-Neural Approachesltiple, interacting factors and differ depending on the NLG task they address. More specifically, three main types of pre-neural NLG architectures can be distinguished depending on whether the task is to generate from data from meaning representations or text.

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Generating Better Textome data), is first encoded into a continuous representation. This representation is then input to the decoder, which predicts output words, one step at a time, conditioned both on the input representation and on the previously predicted words.