Constructing interval variables via faceted Rasch measurement and multitask deep learning: a hate speech application

By Chris J. Kennedy, Geoff Bacon, A. Sahn, Claudia von Vacano. 2020

115 citations recorded by the source corpus.

We propose a system for measuring hate speech on a continuous, interval-valued spectrum ranging from genocidal to supportive speech by combining supervised deep learning with faceted Rasch item response theory (IRT). We decompose the theoretical construct of hate speech into constituent concepts operationalized as 10 ordinal labels. Those labels are reconstituted via IRT probabilistic latent modeling into an interval outcome measure while simultaneously estimating and adjusting for each annotator's labeling perspective. Our scaling procedure integrates naturally with a multitask deep learning architecture for automated prediction, allowing design-based explainability of the continuous score through those components. We apply this method to a new, open source dataset of 50,070 social media comments sourced from YouTube, Twitter, and Reddit, annotated and labeled by 11,143 United States-based Amazon Mechanical Turk workers. Our RoBERTa-based model shows improved accuracy compared to alternative approaches. This system offers a new paradigm for supervised NLP that encourages continuous rather than binary constructs, and design-based incorporation of annotator perspective and model explainability.

Subfields: Artificial Intelligence, Information Systems, Natural Language Processing

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Citations in the corpus

Detoxifying Language Models Risks Marginalizing Minority Voices (2021, background): “A natural direction for future work is to thus improve datasets, for example, by changing the annotation procedure (Sap et al., 2019) or labeling scheme (Kennedy et al., 2020; Sap et al., 2020).”
How True is GPT-2? An Empirical Analysis of Intersectional Occupational Biases (2021, background): “These learned associations can persist in tasks such as machine translation (Stanovsky et al., 2019), dialogue systems (Liu et al., 2020), and hate speech classi-ﬁers (Kennedy et al., 2020).”