Название: Pretrained Transformers for Text Ranking: Bert and BeyondАвтор: Jimmy Lin, Rodrigo Nogueira
Издательство: Morgan & Claypool
Год: 2022
Страниц: 327
Язык: английский
Формат: pdf (true)
Размер: 10.15 MB
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.
This book provides an overview of text ranking with a family of neural network models known as transformers, of which BERT (Bidirectional Encoder Representations from Transformers), an invention of Google, is the best-known example. These models have been responsible for a paradigm shift in the fields of natural language processing (NLP) and information retrieval (IR) and, more broadly, human language technologies (HLT), a catch-all term that includes technologies to process, analyze, and otherwise manipulate (human) language data. There are few endeavors involving the automatic processing of natural language that remain untouched by BERT. In the context of text ranking, BERT provides results that are undoubtedly superior in quality than what came before. This is a robust and widely replicated empirical result, across many text ranking tasks, domains, and problem formulations.
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