This book covers the main research areas that bridge the world of databases and SQL with the world of natural language. The objective is to provide comprehensive coverage of the most influential work in the field.

After an introductory chapter on the history of natural language interfaces to databases (NLIDBs) and a brief neural primer on deep learning architectures frequently mentioned throughout the book, the subsequent chapters 2, 3, 4 then focus on the Text-to-SQL problem. There, an overview of the problem is given, followed by a general architecture of Text-to-SQL systems and a deeper analysis of specific systems. Additionally, the reverse process of explaining an SQL query (i.e., SQL-to-Text) is examined in chapter 5, along with open research problems and the currently available solutions. Next, chapter 6 provides an overview of the multi-turn Text-to-SQL problem, outlines the underlying system architectures, and introduces key representative systems. Chapter 7 takes a broader look at the more general areas of code understanding and generation that encapsulate the problems discussed in the previous chapters. Moving on, chapters 8 and 9 focus on generating NL explanations and summaries of data (i.e., the Data-to-Text problem), offering an overview of the problem and its challenges as well as an overall system architecture and specific Data-to-Text systems. Chapter 10 brings Data-to-Text to NLIDBs by diving deeper into the Results-to-Text problem that focuses on how to express the result of a query in user-friendly natural language. Eventually, Chapter 11 concludes the book by offering insights into how all the discussed research areas and systems can be brought together in order to create an NLIDB, along with risk and challenges that must be considered in the process.

This book is intended for both researchers and practitioners interested in NLIDBs, regardless of their prior familiarity with the topic. Readers with experience in this area will benefit from a structured overview and categorization of existing systems, along with an in-depth analysis of benchmarks, persistent challenges, and open research questions. Conversely, newcomers can explore the landscape of neural NLIDBs through an accessible presentation of the relevant subfields and key advancements, without requiring any prior background knowledge.