| 書目名稱 | Pretrained Transformers for Text Ranking | | 副標題 | BERT and Beyond | | 編輯 | Jimmy Lin,Rodrigo Nogueira,Andrew Yates | | 視頻video | http://file.papertrans.cn/755/754963/754963.mp4 | | 叢書名稱 | Synthesis Lectures on Human Language Technologies | | 圖書封面 |  | | 描述 | The goal of text ranking is to generate an ordered list of texts retrieved from a corpus in response to a query. Although the most common formulation of text ranking is search, instances of the task can also be found in many natural language processing (NLP) applications.This book provides an overview of text ranking with neural network architectures known as transformers, of which BERT (Bidirectional Encoder Representations from Transformers) is the best-known example. The combination of transformers and self-supervised pretraining has been responsible for a paradigm shift in NLP, information retrieval (IR), and beyond. This book provides a synthesis of existing work as a single point of entry for practitioners who wish to gain a better understanding of how to apply transformers to text ranking problems and researchers who wish to pursue work in this area. It covers a wide range of modern techniques, grouped into two high-level categories: transformer models that perform reranking inmulti-stage architectures and dense retrieval techniques that perform ranking directly. Two themes pervade the book: techniques for handling long documents, beyond typical sentence-by-sentence processi | | 出版日期 | Book 2022 | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-031-02181-7 | | isbn_softcover | 978-3-031-01053-8 | | isbn_ebook | 978-3-031-02181-7Series ISSN 1947-4040 Series E-ISSN 1947-4059 | | issn_series | 1947-4040 | | copyright | Springer Nature Switzerland AG 2022 |
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