A Survey of Controllable Text Generation using Transformer-based Pre-trained Language Models

TL;DR

This survey reviews recent advances in controllable text generation using transformer-based pre-trained language models, highlighting key tasks, approaches, evaluation methods, challenges, and future directions in this rapidly evolving field.

Contribution

It is the first comprehensive survey summarizing state-of-the-art CTG techniques with transformer-based PLMs, providing a landscape and roadmap for future research.

Findings

Diverse approaches have emerged in recent 3-4 years for CTG.

Various evaluation methods are used to assess controllability and quality.

Challenges include interpretability and ensuring controllability in neural models.

Abstract

Controllable Text Generation (CTG) is emerging area in the field of natural language generation (NLG). It is regarded as crucial for the development of advanced text generation technologies that better meet the specific constraints in practical applications. In recent years, methods using large-scale pre-trained language models (PLMs), in particular the widely used transformer-based PLMs, have become a new paradigm of NLG, allowing generation of more diverse and fluent text. However, due to the limited level of interpretability of deep neural networks, the controllability of these methods need to be guaranteed. To this end, controllable text generation using transformer-based PLMs has become a rapidly growing yet challenging new research hotspot. A diverse range of approaches have emerged in the recent 3-4 years, targeting different CTG tasks that require different types of controlled constraints. In this paper, we present a systematic critical review on the common tasks, main approaches, and evaluation methods in this area. Finally, we discuss the challenges that the field is facing, and put forward various promising future directions. To the best of our knowledge, this is the first survey paper to summarize the state-of-the-art CTG techniques from the perspective of Transformer-based PLMs. We hope it can help researchers and practitioners in the related fields to quickly track the academic and technological frontier, providing them with a landscape of the area and a roadmap for future research.