Название: Deep Learning Approaches to Text Production Автор: Shashi Narayan, Claire Gardent
Издательство: Morgan & Claypool Publishers
Год: 2020
Страниц: 201
Язык: английский
Формат: pdf (true)
Размер: 10.1 MB
Text production has many applications. It is used, for instance, to generate dialogue turns from dialogue moves, verbalise the content of knowledge bases, or generate English sentences from rich linguistic representations, such as dependency trees or abstract meaning representations. Text production is also at work in text-to-text transformations such as sentence compression, sentence fusion, paraphrasing, sentence (or text) simplification, and text summarisation. This book offers an overview of the fundamentals of neural models for text production. In particular, we elaborate on three main aspects of neural approaches to text production: how sequential decoders learn to generate adequate text, how encoders learn to produce better input representations, and how neural generators account for task-specific objectives.
Indeed, each text-production task raises a slightly different challenge (e.g, how to take the dialogue context into account when producing a dialogue turn, how to detect and merge relevant information when summarising a text, or how to produce a well-formed text that correctly captures the information contained in some input data in the case of data-to-text generation). We outline the constraints specific to some of these tasks and examine how existing neural models account for them. More generally, this book considers text-to-text, meaning-to-text, and data-to-text transformations. It aims to provide the audience with a basic knowledge of neural approaches to text production and a roadmap to get them started with the related work. The book is mainly targeted at researchers, graduate students, and industrials interested in text production from different forms of inputs.
In recent years, deep learning, also called the neural approach, has been proposed for text production. The pre-neural approach generally relied on a pipeline 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 generation process (generation) generates an output text using the input representation. One of the main strengths of neural networks is that they provide an amazing tool for representation learning.
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