Neural Language Generation: Formulation, Methods, and Evaluation

TL;DR

This survey reviews the formulation, methods, and evaluation techniques of neural natural language generation, highlighting recent advances, challenges, and the need for standardized assessment approaches in the rapidly evolving field.

Contribution

It provides a comprehensive categorization of NLG problems, reviews neural architectures and methods, and discusses evaluation challenges and current approaches.

Findings

Neural models can generate coherent and diverse texts.

Evaluation of generated text remains a significant challenge.

Various neural architectures are employed across different NLG tasks.

Abstract

Recent advances in neural network-based generative modeling have reignited the hopes in having computer systems capable of seamlessly conversing with humans and able to understand natural language. Neural architectures have been employed to generate text excerpts to various degrees of success, in a multitude of contexts and tasks that fulfil various user needs. Notably, high capacity deep learning models trained on large scale datasets demonstrate unparalleled abilities to learn patterns in the data even in the lack of explicit supervision signals, opening up a plethora of new possibilities regarding producing realistic and coherent texts. While the field of natural language generation is evolving rapidly, there are still many open challenges to address. In this survey we formally define and categorize the problem of natural language generation. We review particular application tasks that are instantiations of these general formulations, in which generating natural language is of practical importance. Next we include a comprehensive outline of methods and neural architectures employed for generating diverse texts. Nevertheless, there is no standard way to assess the quality of text produced by these generative models, which constitutes a serious bottleneck towards the progress of the field. To this end, we also review current approaches to evaluating natural language generation systems. We hope this survey will provide an informative overview of formulations, methods, and assessments of neural natural language generation.