Multi-Modal Discussion Transformer: Integrating Text, Images and Graph Transformers to Detect Hate Speech on Social Media

1 code implementation • 18 Jul 2023 • Liam Hebert, Gaurav Sahu, Yuxuan Guo, Nanda Kishore Sreenivas, Lukasz Golab, Robin Cohen

We present the Multi-Modal Discussion Transformer (mDT), a novel methodfor detecting hate speech in online social networks such as Reddit discussions.

Qualitative Analysis of a Graph Transformer Approach to Addressing Hate Speech: Adapting to Dynamically Changing Content

no code implementations • 25 Jan 2023 • Liam Hebert, Hong Yi Chen, Robin Cohen, Lukasz Golab

Included is an exploration of the kinds of posts that permeate social media today, including the use of hateful images.

Hate Speech Detection

Predicting Hateful Discussions on Reddit using Graph Transformer Networks and Communal Context

1 code implementation • 10 Jan 2023 • Liam Hebert, Lukasz Golab, Robin Cohen

We propose a system to predict harmful discussions on social media platforms.

Robust Candidate Generation for Entity Linking on Short Social Media Texts

no code implementations • COLING (WNUT) 2022 • Liam Hebert, Raheleh Makki, Shubhanshu Mishra, Hamidreza Saghir, Anusha Kamath, Yuval Merhav

Entity Linking (EL) is the gateway into Knowledge Bases.

Entity Linking NER +2

FedFormer: Contextual Federation with Attention in Reinforcement Learning

1 code implementation • 27 May 2022 • Liam Hebert, Lukasz Golab, Pascal Poupart, Robin Cohen

We evaluate our methods on the Meta-World environment and find that our approach yields significant improvements over FedAvg and non-federated Soft Actor-Critic single-agent methods.

Federated Learning reinforcement-learning +1