Abstract
Chatbots have the risk of generating offensive utterances, which must be avoided. Post-deployment, one way for a chatbot to continuously improve is to source utterance/label pairs from feedback by live users. However, among users are trolls, who provide training examples with incorrect labels. To de-troll training data, previous work removed training examples that have high user-aggregated cross-validation (CV) error. However, CV is expensive; and in a coordinated attack, CV may be overwhelmed by trolls in number and in consistency among themselves. In the present work, I address both limitations by proposing a solution inspired by methodology in automated essay scoring (AES): have multiple users rate each utterance, then perform latent class analysis (LCA) to infer correct labels. As it does not require GPU computations, LCA is inexpensive. In experiments, I found that the AES-like solution can infer training labels with high accuracy when trolls are consistent, even when trolls are the majority.- Anthology ID:
- 2023.findings-emnlp.928
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2023
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Houda Bouamor, Juan Pino, Kalika Bali
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 13893–13899
- Language:
- URL:
- https://preview.aclanthology.org/add_missing_videos/2023.findings-emnlp.928/
- DOI:
- 10.18653/v1/2023.findings-emnlp.928
- Cite (ACL):
- Michael Ilagan. 2023. Learning to love diligent trolls: Accounting for rater effects in the dialogue safety task. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 13893–13899, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- Learning to love diligent trolls: Accounting for rater effects in the dialogue safety task (Ilagan, Findings 2023)
- PDF:
- https://preview.aclanthology.org/add_missing_videos/2023.findings-emnlp.928.pdf