Multitask Learning for Emotionally Analyzing Sexual Abuse Disclosures
Ramit Sawhney, Puneet Mathur, Taru Jain, Akash Kumar Gautam, Rajiv Ratn Shah
Abstract
The #MeToo movement on social media platforms initiated discussions over several facets of sexual harassment in our society. Prior work by the NLP community for automated identification of the narratives related to sexual abuse disclosures barely explored this social phenomenon as an independent task. However, emotional attributes associated with textual conversations related to the #MeToo social movement are complexly intertwined with such narratives. We formulate the task of identifying narratives related to the sexual abuse disclosures in online posts as a joint modeling task that leverages their emotional attributes through multitask learning. Our results demonstrate that positive knowledge transfer via context-specific shared representations of a flexible cross-stitched parameter sharing model helps establish the inherent benefit of jointly modeling tasks related to sexual abuse disclosures with emotion classification from the text in homogeneous and heterogeneous settings. We show how for more domain-specific tasks related to sexual abuse disclosures such as sarcasm identification and dialogue act (refutation, justification, allegation) classification, homogeneous multitask learning is helpful, whereas for more general tasks such as stance and hate speech detection, heterogeneous multitask learning with emotion classification works better.- Anthology ID:
- 2021.naacl-main.387
- Volume:
- Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
- Month:
- June
- Year:
- 2021
- Address:
- Online
- Editors:
- Kristina Toutanova, Anna Rumshisky, Luke Zettlemoyer, Dilek Hakkani-Tur, Iz Beltagy, Steven Bethard, Ryan Cotterell, Tanmoy Chakraborty, Yichao Zhou
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 4881–4892
- Language:
- URL:
- https://aclanthology.org/2021.naacl-main.387
- DOI:
- 10.18653/v1/2021.naacl-main.387
- Cite (ACL):
- Ramit Sawhney, Puneet Mathur, Taru Jain, Akash Kumar Gautam, and Rajiv Ratn Shah. 2021. Multitask Learning for Emotionally Analyzing Sexual Abuse Disclosures. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 4881–4892, Online. Association for Computational Linguistics.
- Cite (Informal):
- Multitask Learning for Emotionally Analyzing Sexual Abuse Disclosures (Sawhney et al., NAACL 2021)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-5/2021.naacl-main.387.pdf
- Code
- midas-research/metoo-mtl-naacl