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How to control sentiment in text generation: a survey of the state-of-the-art in sentiment-control techniques

Lorandi, Michela and Belz, Anya (2023) How to control sentiment in text generation: a survey of the state-of-the-art in sentiment-control techniques. In: 13th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis, 14 July 2023, Toronto, Canada.

Abstract
Recent advances in the development of large Pretrained Language Models, such as GPT, BERT and Bloom, have achieved remarkable performance on a wide range of different NLP tasks. However, when used for text generation tasks, these models still have limitations when it comes to controlling the content and style of the generated text, often producing content that is incorrect, irrelevant, or inappropriate in the context of a given task. In this survey paper, we explore methods for controllable text generation with a focus on sentiment control. We systematically collect papers from the ACL Anthology, create a categorisation scheme based on different control techniques and controlled attributes, and use the scheme to categorise and compare methods. The result is a detailed and comprehensive overview of state-of-the-art techniques for sentiment-controlled text generation categorised on the basis of how the control is implemented and what attributes are controlled and providing a clear idea of their relative strengths and weaknesses.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Workshop
Refereed:Yes
Uncontrolled Keywords:Natural Language Generation; Controlled Text Generation
Subjects:Computer Science > Artificial intelligence
Computer Science > Computational linguistics
Computer Science > Machine learning
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Initiatives and Centres > ADAPT
Published in: Proceedings of the 13th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis. . Association for Computational Linguistics (ACL).
Publisher:Association for Computational Linguistics (ACL)
Official URL:https://aclanthology.org/2023.wassa-1.30
Copyright Information:© 2023 ACL
Funders:Science Foundation Ireland Centre for Research Training in Digitally-Enhanced Reality (d-real) under Grant No.18/CRT/6224, Science Foundation Ireland under Grant Agreement No.13/RC/2106_P2at the ADAPTSFI Research Centre at Dublin City University.
ID Code:28858
Deposited On:31 Jul 2023 13:00 by Michela Lorandi . Last Modified 31 Jul 2023 13:00
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