A Survey of Knowledge-Enhanced Text Generation

TL;DR

This survey reviews recent advances in knowledge-enhanced text generation, highlighting methods, architectures, and applications that incorporate external knowledge to improve NLP output quality.

Contribution

It provides a comprehensive overview of the latest research on integrating various forms of knowledge into text generation models over the past five years.

Findings

Summarizes general methods and architectures for knowledge integration.

Categorizes techniques based on different knowledge data forms.

Highlights applications across academia and industry.

Abstract

The goal of text generation is to make machines express in human language. It is one of the most important yet challenging tasks in natural language processing (NLP). Since 2014, various neural encoder-decoder models pioneered by Seq2Seq have been proposed to achieve the goal by learning to map input text to output text. However, the input text alone often provides limited knowledge to generate the desired output, so the performance of text generation is still far from satisfaction in many real-world scenarios. To address this issue, researchers have considered incorporating various forms of knowledge beyond the input text into the generation models. This research direction is known as knowledge-enhanced text generation. In this survey, we present a comprehensive review of the research on knowledge enhanced text generation over the past five years. The main content includes two parts: (i) general methods and architectures for integrating knowledge into text generation; (ii) specific techniques and applications according to different forms of knowledge data. This survey can have broad audiences, researchers and practitioners, in academia and industry.