Reducing Non-Normative Text Generation from Language Models

Xiangyu Peng, Siyan Li, Spencer Frazier, Mark Riedl


Abstract
Large-scale, transformer-based language models such as GPT-2 are pretrained on diverse corpora scraped from the internet. Consequently, they are prone to generating non-normative text (i.e. in violation of social norms). We introduce a technique for fine-tuning GPT-2, using a policy gradient reinforcement learning technique and a normative text classifier to produce reward and punishment values. We evaluate our technique on five data sets using automated and human participant experiments. The normative text classifier is 81-90% accurate when compared to gold-standard human judgements of normative and non-normative generated text. Our normative fine-tuning technique is able to reduce non-normative text by 27-61%, depending on the data set.
Anthology ID:
2020.inlg-1.43
Volume:
Proceedings of the 13th International Conference on Natural Language Generation
Month:
December
Year:
2020
Address:
Dublin, Ireland
Venue:
INLG
SIG:
SIGGEN
Publisher:
Association for Computational Linguistics
Note:
Pages:
374–383
Language:
URL:
https://aclanthology.org/2020.inlg-1.43
DOI:
Bibkey:
Cite (ACL):
Xiangyu Peng, Siyan Li, Spencer Frazier, and Mark Riedl. 2020. Reducing Non-Normative Text Generation from Language Models. In Proceedings of the 13th International Conference on Natural Language Generation, pages 374–383, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
Reducing Non-Normative Text Generation from Language Models (Peng et al., INLG 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.inlg-1.43.pdf
Data
ROCStories