Abstract
While implicit embeddings so far have been mostly concerned with creating an overall representation of the user, we evaluate a different approach. By only considering content directed at a specific topic, we create sub-user embeddings, and measure their usefulness on the tasks of sarcasm and hate speech detection. In doing so, we show that task-related topics can have a noticeable effect on model performance, especially when dealing with intended expressions like sarcasm, but less so for hate speech, which is usually labelled as such on the receiving end.- Anthology ID:
- 2022.nlpcss-1.14
- Volume:
- Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science (NLP+CSS)
- Month:
- November
- Year:
- 2022
- Address:
- Abu Dhabi, UAE
- Editors:
- David Bamman, Dirk Hovy, David Jurgens, Katherine Keith, Brendan O'Connor, Svitlana Volkova
- Venue:
- NLP+CSS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 126–139
- Language:
- URL:
- https://aclanthology.org/2022.nlpcss-1.14
- DOI:
- 10.18653/v1/2022.nlpcss-1.14
- Cite (ACL):
- Kim Breitwieser. 2022. Can Contextualizing User Embeddings Improve Sarcasm and Hate Speech Detection?. In Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science (NLP+CSS), pages 126–139, Abu Dhabi, UAE. Association for Computational Linguistics.
- Cite (Informal):
- Can Contextualizing User Embeddings Improve Sarcasm and Hate Speech Detection? (Breitwieser, NLP+CSS 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2022.nlpcss-1.14.pdf