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

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書目名稱Deep Learning Approaches to Text Production
編輯Shashi Narayan,Claire Gardent
視頻videohttp://file.papertrans.cn/265/264571/264571.mp4
叢書名稱Synthesis Lectures on Human Language Technologies
圖書封面Titlebook: Deep Learning Approaches to Text Production;  Shashi Narayan,Claire Gardent Book 2020 Springer Nature Switzerland AG 2020
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
doihttps://doi.org/10.1007/978-3-031-02173-2
isbn_softcover978-3-031-01045-3
isbn_ebook978-3-031-02173-2Series ISSN 1947-4040 Series E-ISSN 1947-4059
issn_series 1947-4040
copyrightSpringer Nature Switzerland AG 2020
The information of publication is updating

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Modelling Task-Specific Communication Goals, we will discuss how communication goal-oriented generators can be useful for text production. In particular, we will focus on generators that are specifically trained for summarisation, simplification, to profile user for dialogue-response generation, or to generate from loosely aligned data.
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Book 2020ases, 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
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1947-4040 ising 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).978-3-031-01045-3978-3-031-02173-2Series ISSN 1947-4040 Series E-ISSN 1947-4059
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