Multi-task learning for hate speech detection

dc.contributor.advisorLease, Matthew A.
dc.creatorLee, Sooyong
dc.creator.orcid0000-0002-4093-6546
dc.date.accessioned2023-06-02T21:21:19Z
dc.date.available2023-06-02T21:21:19Z
dc.date.created2023-05
dc.date.issued2023-04-21
dc.date.submittedMay 2023
dc.date.updated2023-06-02T21:21:20Z
dc.description.abstractAmidst the proliferation of social media and the accompanying explosion of information and content generation, the amount of online hate speech has grown rapidly. In efforts to build and train hate speech detection models to counter this, datasets have been annotated for hate speech. However, there exists incompatibility of categories of hate speech across different datasets, the lack of clear and ubiquitous definitions for hate speech, and generalization issues of models which depend highly on training data. To address this, we propose framing hate speech detection as multi-task learning (MTL) which provides a natural and principled way for a model to specialize on dataset-specific hate speech detection tasks while leveraging shared notions of hate speech across datasets to acquire more general notions of hate speech.
dc.description.departmentComputer Sciences
dc.format.mimetypeapplication/pdf
dc.identifier.urihttps://hdl.handle.net/2152/119144
dc.identifier.urihttp://dx.doi.org/10.26153/tsw/46022
dc.language.isoen
dc.subjectMulti-task learning
dc.subjectHate speech
dc.titleMulti-task learning for hate speech detection
dc.typeThesis
dc.type.materialtext
thesis.degree.departmentComputer Sciences
thesis.degree.disciplineComputer Science
thesis.degree.grantorThe University of Texas at Austin
thesis.degree.levelMasters
thesis.degree.nameMaster of Science in Computer Sciences
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