Empirical Analysis of Multi-Task Learning for Reducing Model Bias in Toxic Comment Detection

Empirical Analysis of Multi-T ask Learning for Reducing Model Bias in T oxic Comment Detection Ameya V aidya, 1 Feng Mai, 2 Y ue Ning 3 1 Bridgewater-Raritan Regional High School 2 School of Business, Stevens Institute of Technology 3 Department of Computer Science, Stevens Institute of Technology ameyav993@gmail.com, Abstract With the recent rise of toxicity in online conversations on social media platforms, using modern machine learning algorithms for toxic comment detection has become a central focus of many online applications. Researchers and companies have developed a variety of shallow and deep learning models to identify toxicity in online conversations, reviews, or comments with mixed successes. However, these existing approaches have learned to incorrectly associate nontoxic comments that have certain trigger-words (e.g. In this paper, we evaluate dozens of state-of-the-art models with the specific focus of reducing model bias towards these commonly-attacked identity groups. We propose a multi-task learning model with an attention layer that jointly learns to predict the toxicity of a comment as well as the identities present in the comments in order to reduce this bias. We then compare our model to an array of shallow and deep-learning models using metrics designed especially to test for unintended model bias within these identity groups. Introduction The identification of potential toxicity within online conversations has always been a significant task for current platform providers. Toxic comments have the unfortunate effect of causing users to leave a discussion or give up sharing their perspective and can give a bad reputation to platforms where these discussions take place. Twitter's CEO reaffirmed that Twitter is still being overrun by spam, abuse, and misinformation. Current research involves investigating common challenges in toxic comment classification (van Aken et al. 2018), identifying subtle forms of toxicity (Noever 2018), detecting early signs of toxicity (Zhang et al. 2018), and analysing sarcasm within conversations (Ghosh, Fabbri, and Muresan 2018).