Deep Learning Approaches to Text Production

Text production (Reiter and Dale 2000; Gatt and Krahmer 2018) is also referred to as natural language generation (NLG). It is a subtask of natural language processing focusing on the generation of natural language text. Although as important as natural language understanding for communication, NLG had received relatively less research attention. Recently, the rise of deep learning techniques has led to a surge of research interest in text production, both in general and for specific applications such as text summarization and dialogue systems. Deep learning allows NLG models to be constructed based on neural representations, thereby enabling end-to-end NLG systems to replace traditional pipeline approaches, which frees us from tedious engineering efforts and improves the output quality. In particular, a neural encoder-decoder structure (Cho et al. 2014; Sutskever, Vinyals, and Le 2014) has been widely used as a basic framework, which computes input representations using a neural encoder, according to which a text sequence is generated token by token using a neural decoder. Very recently, pre-training techniques (Broscheit et al. 2010; Radford 2018; Devlin et al. 2019) have further allowed neural models to collect knowledge from large raw text data, further improving the quality of both encoding and decoding. This book introduces the fundamentals of neural text production, discussing both the mostly investigated tasks and the foundational neural methods. NLG tasks with different types of inputs are introduced, and benchmark datasets are discussed in detail. The encoder-decoder architecture is introduced together with basic neural network components such as convolutional neural network (CNN) (Kim 2014) and recurrent neural network (RNN) (Cho et al. 2014). Elaborations are given on the encoder, the decoder, and task-specific optimization techniques. A contrast is made between the neural solution and traditional solutions to the task. Toward the end of the book, more recent techniques such as self-attention networks (Vaswani et al. 2017) and pre-training are briefly discussed. Throughout the book, figures are given to facilitate understanding and references are provided to enable further reading. Chapter 1 introduces the task of text production, discussing three typical input settings, namely, generation from meaning representations (MR; i.e., realization), generation from data (i.e., data-to-text), and generation from text (i.e., text-to-text). At the end of the chapter, a book outline is given, and the scope, coverage, and notation convention