SemEval-2022 Task 8: Multilingual news article similarity

no code implementations • SemEval (NAACL) 2022 • Xi Chen, Ali Zeynali, Chico Camargo, Fabian Flöck, Devin Gaffney, Przemyslaw Grabowicz, Scott Hale, David Jurgens, Mattia Samory

Thousands of new news articles appear daily in outlets in different languages.

Recalibrating classifiers for interpretable abusive content detection

no code implementations • EMNLP (NLP+CSS) 2020 • Bertie Vidgen, Scott Hale, Sam Staton, Tom Melham, Helen Margetts, Ohad Kammar, Marcin Szymczak

We investigate the use of machine learning classifiers for detecting online abuse in empirical research.

Probabilistic Programming

Detecting East Asian Prejudice on Social Media

1 code implementation • EMNLP (ALW) 2020 • Bertie Vidgen, Austin Botelho, David Broniatowski, Ella Guest, Matthew Hall, Helen Margetts, Rebekah Tromble, Zeerak Waseem, Scott Hale

The outbreak of COVID-19 has transformed societies across the world as governments tackle the health, economic and social costs of the pandemic.

Room to Glo: A Systematic Comparison of Semantic Change Detection Approaches with Word Embeddings

no code implementations • IJCNLP 2019 • Philippa Shoemark, Farhana Ferdousi Liza, Dong Nguyen, Scott Hale, Barbara McGillivray

Word embeddings are increasingly used for the automatic detection of semantic change; yet, a robust evaluation and systematic comparison of the choices involved has been lacking.

Change Detection Time Series +1

Challenges and frontiers in abusive content detection

1 code implementation • WS 2019 • Bertie Vidgen, Alex Harris, Dong Nguyen, Rebekah Tromble, Scott Hale, Helen Margetts

Online abusive content detection is an inherently difficult task.

Abuse Detection