| 书目名称 | Deep Learning Approaches to Text Production | | 编辑 | Shashi Narayan,Claire Gardent | | 视频video | | | 丛书名称 | Synthesis Lectures on Human Language Technologies | | 图书封面 |  | | 描述 | .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, eachtext-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). | | 出版日期 | Book 2020 | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-031-02173-2 | | isbn_softcover | 978-3-031-01045-3 | | isbn_ebook | 978-3-031-02173-2Series ISSN 1947-4040 Series E-ISSN 1947-4059 | | issn_series | 1947-4040 | | copyright | Springer Nature Switzerland AG 2020 |
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发表于 2025-3-21 16:22:35
