LINGUIST List 32.3734

Tue Nov 30 2021

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

Editor for this issue: Billy Dickson <>

Date: 16-Nov-2021
From: Brent Beckley <>
Subject: Pretrained Transformers for Text Ranking: Lin, Nogueira, Yates
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Title: Pretrained Transformers for Text Ranking
Subtitle: BERT and Beyond
Series Title: Synthesis Lectures on Human Language Technologies
Published: 2021
Publisher: Morgan & Claypool Publishers

Book URL:

Author: Jimmy Lin
Author: Rodrigo Nogueira
Author: Andrew Yates
Paperback: ISBN: 9781636392288 Pages: 325 Price: ---- 89.95

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)

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Page Updated: 30-Nov-2021