Abstract
To proactively offer social media users a safe online experience, there is a need for systems that can detect harmful posts and promptly alert platform moderators. In order to guarantee the enforcement of a consistent policy, moderators are provided with detailed guidelines. In contrast, most state-of-the-art models learn what abuse is from labeled examples and as a result base their predictions on spurious cues, such as the presence of group identifiers, which can be unreliable. In this work we introduce the concept of policy-aware abuse detection, abandoning the unrealistic expectation that systems can reliably learn which phenomena constitute abuse from inspecting the data alone. We propose a machine-friendly representation of the policy that moderators wish to enforce, by breaking it down into a collection of intents and slots. We collect and annotate a dataset of 3,535 English posts with such slots, and show how architectures for intent classification and slot filling can be used for abuse detection, while providing a rationale for model decisions.1- Anthology ID:
- 2022.tacl-1.82
- Volume:
- Transactions of the Association for Computational Linguistics, Volume 10
- Month:
- Year:
- 2022
- Address:
- Cambridge, MA
- Editors:
- Brian Roark, Ani Nenkova
- Venue:
- TACL
- SIG:
- Publisher:
- MIT Press
- Note:
- Pages:
- 1440–1454
- Language:
- URL:
- https://aclanthology.org/2022.tacl-1.82
- DOI:
- 10.1162/tacl_a_00527
- Cite (ACL):
- Agostina Calabrese, Björn Ross, and Mirella Lapata. 2022. Explainable Abuse Detection as Intent Classification and Slot Filling. Transactions of the Association for Computational Linguistics, 10:1440–1454.
- Cite (Informal):
- Explainable Abuse Detection as Intent Classification and Slot Filling (Calabrese et al., TACL 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/2022.tacl-1.82.pdf