Chris Kennedy will be giving a virtual talk on Friday, September 25th from 11am — 12pm. Zoom information will be distributed via the Berkeley NLP Seminar listserv. [Update: The preprint for this talk is now available.]

Title: Constructing Interval Variables via Faceted Rasch Measurement and Multitask Deep Learning: A Hate Speech Application

Abstract: We propose a general method for measuring complex variables on a continuous, interval spectrum by combining supervised deep learning with the Constructing Measures approach to faceted Rasch item response theory (IRT). We decompose the target construct, hate speech, into multiple constituent components that are labeled as ordinal survey items by human reviewers. Those survey responses are transformed via an IRT nonlinear activation into a debiased, continuous outcome measure. Our method estimates the survey interpretation bias of the human labelers and eliminates that influence on the generated continuous measure. We further estimate the response quality of individual labelers using faceted IRT, allowing low-quality labels to be removed or down-weighted.

Our faceted Rasch scaling procedure integrates naturally with a multitask, weight-sharing deep learning architecture for automated prediction on new data. The ratings on the theorized components of the target outcome are used as supervised, ordinal latent variables for the neural networks’ internal concept learning, improving adversarial robustness and promoting generalizability. We test the use of a neural activation function (ordinal softmax) and loss function (ordinal cross-entropy) designed to exploit the structure of ordinal outcome variables. Our multitask architecture leads to a new form of model interpretability because each continuous prediction can be directly explained by the constituent components in the penultimate layer.

We demonstrate this new method on a dataset of 50,000 social media comments sourced from YouTube, Twitter, and Reddit and labeled by 10,000 United States-based Amazon Mechanical Turk workers to measure a continuous spectrum from hate speech to counterspeech. We evaluate Universal Sentence Encoders, BERT, and RoBERTa as language representation models for the comment text, and compare our predictive accuracy to Google Jigsaw’s Perspective API models, showing significant improvement over this standard benchmark.

BioChris Kennedy is a postdoctoral fellow in biomedical informatics at Harvard Medical School; he received his PhD in biostatistics from UC Berkeley in 2020. His research interests include targeted causal inference and medical AI. Chris chaired the TextXD: Text Analysis Across Domains conference in 2019 & 2018 as a Berkeley Institute for Data Science fellow. He remains a research associate of D-Lab, the Integrative Cancer Research Group, and Kaiser Permanente’s Division of Research. In 2018 he led data science for the election campaigns of Governor Gavin Newsom and Congresswoman Katie Porter.