32.3734, Books: Pretrained Transformers for Text Ranking: Lin, Nogueira, Yates

[The LINGUIST List]

LINGUIST List: Vol-32-3734. Tue Nov 30 2021. ISSN: 1069 - 4875.

Subject: 32.3734, Books: Pretrained Transformers for Text Ranking: Lin, Nogueira, Yates

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Date: Tue, 30 Nov 2021 12:20:03
From: Brent Beckley [beckley at morganclaypool.com]
Subject: Pretrained Transformers for Text Ranking: Lin, Nogueira, Yates

 


Title: Pretrained Transformers for Text Ranking 
Subtitle: BERT and Beyond 
Series Title: Synthesis Lectures on Human Language Technologies  

Publication Year: 2021 
Publisher: Morgan & Claypool Publishers
	   http://www.morganclaypool.com
	

Book URL: https://www.morganclaypoolpublishers.com/catalog_Orig/product_info.php?products_id=1681 


Author: Jimmy Lin
Author: Rodrigo Nogueira
Author: Andrew Yates

Paperback: ISBN:  9781636392288 Pages: 325 Price: ----  89.95


Abstract:

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 in
multi-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 processing in NLP, and techniques for
addressing the tradeoff between effectiveness (i.e., result quality) and
efficiency (e.g., query latency, model and index size). Although transformer
architectures and pretraining techniques are recent innovations, many aspects
of how they are applied to text ranking are relatively well understood and
represent mature techniques. However, there remain many open research
questions, and thus in addition to laying out the foundations of pretrained
transformers for text ranking, this book also attempts to prognosticate where
the field is heading.
 



Linguistic Field(s): Computational Linguistics


Written In: English  (eng)

See this book announcement on our website: 
http://linguistlist.org/pubs/books/get-book.cfm?BookID=158133




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LINGUIST List: Vol-32-3734	
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