Abstract
Transformer-based Language Models (LMs) have achieved impressive results on natural language understanding tasks, but they can also generate toxic text such as insults, threats, and profanity, limiting their real-world applications. To overcome this issue, a few text generation approaches aim to detoxify toxic texts using additional LMs or perturbations. However, previous methods require excessive memory, computations, and time which are serious bottlenecks in their real-world application. To address such limitations, we propose an effective yet efficient method for language detoxification using an attribute-discriminative latent space. Specifically, we project the latent space of an original Transformer LM onto a discriminative latent space that well-separates texts by their attributes using a projection block and an attribute discriminator. This allows the LM to control the text generation to be non-toxic with minimal memory and computation overhead. We validate our model, Attribute-Discriminative Language Model (ADLM) on detoxified language and dialogue generation tasks, on which our method significantly outperforms baselines both in performance and efficiency.- Anthology ID:
- 2023.acl-long.565
- Volume:
- Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 10149–10171
- Language:
- URL:
- https://aclanthology.org/2023.acl-long.565
- DOI:
- Cite (ACL):
- Jin Myung Kwak, Minseon Kim, and Sung Ju Hwang. 2023. Language Detoxification with Attribute-Discriminative Latent Space. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 10149–10171, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Language Detoxification with Attribute-Discriminative Latent Space (Kwak et al., ACL 2023)
- PDF:
- https://preview.aclanthology.org/nodalida-main-page/2023.acl-long.565.pdf