Multitask Learning for Emotionally Analyzing Sexual Abuse Disclosures

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. 1

DocTime: A Document-level Temporal Dependency Graph Parser

“…Future works will aim to explore more ways for integrating temporal dependency graphs into neural architectures across different application domains. In future, we would like to explore temporal event mining to aid various social media applications such as improving hate speech detection (Mathur et al, 2018b;, analyzing temporality in suicidal ideation detection (Mishra et al, 2019; and abuse detection (Gautam et al, 2020;Sawhney et al, 2021). The proposed Time-Transformer can find applications in augmenting financial tasks , affective computing (Mittal et al, 2021), and AI for social good (Mathur et al, 2018a) with temporal common sense reasoning.…”