no code implementations • ACL (WOAH) 2021 • Lambert Mathias, Shaoliang Nie, Aida Mostafazadeh Davani, Douwe Kiela, Vinodkumar Prabhakaran, Bertie Vidgen, Zeerak Waseem
We present the results and main findings of the shared task at WOAH 5 on hateful memes detection.
no code implementations • 10 Aug 2022 • Jackson Trager, Alireza S. Ziabari, Aida Mostafazadeh Davani, Preni Golazizian, Farzan Karimi-Malekabadi, Ali Omrani, Zhihe Li, Brendan Kennedy, Nils Karl Reimer, Melissa Reyes, Kelsey Cheng, Mellow Wei, Christina Merrifield, Arta Khosravi, Evans Alvarez, Morteza Dehghani
Moral framing and sentiment can affect a variety of online and offline behaviors, including donation, pro-environmental action, political engagement, and even participation in violent protests.
no code implementations • 28 Oct 2021 • Aida Mostafazadeh Davani, Mohammad Atari, Brendan Kennedy, Morteza Dehghani
Social stereotypes negatively impact individuals' judgements about different groups and may have a critical role in how people understand language directed toward minority social groups.
no code implementations • 12 Oct 2021 • Aida Mostafazadeh Davani, Mark Díaz, Vinodkumar Prabhakaran
Majority voting and averaging are common approaches employed to resolve annotator disagreements and derive single ground truth labels from multiple annotations.
no code implementations • EMNLP (LAW, DMR) 2021 • Vinodkumar Prabhakaran, Aida Mostafazadeh Davani, Mark Díaz
A common practice in building NLP datasets, especially using crowd-sourced annotations, involves obtaining multiple annotator judgements on the same data instances, which are then flattened to produce a single "ground truth" label or score, through majority voting, averaging, or adjudication.
no code implementations • ACL (WOAH) 2021 • Aida Mostafazadeh Davani, Ali Omrani, Brendan Kennedy, Mohammad Atari, Xiang Ren, Morteza Dehghani
By applying logit pairing to equalize outcomes on the restricted set of counterfactuals for each instance, we improve fairness metrics while preserving model performance on hate speech detection.
no code implementations • NAACL 2021 • Xisen Jin, Francesco Barbieri, Brendan Kennedy, Aida Mostafazadeh Davani, Leonardo Neves, Xiang Ren
Fine-tuned language models have been shown to exhibit biases against protected groups in a host of modeling tasks such as text classification and coreference resolution.
no code implementations • 24 Oct 2020 • Aida Mostafazadeh Davani, Ali Omrani, Brendan Kennedy, Mohammad Atari, Xiang Ren, Morteza Dehghani
Counterfactual token fairness for a mentioned social group evaluates the model's predictions as to whether they are the same for (a) the actual sentence and (b) a counterfactual instance, which is generated by changing the mentioned social group in the sentence.
3 code implementations • ACL 2020 • Brendan Kennedy, Xisen Jin, Aida Mostafazadeh Davani, Morteza Dehghani, Xiang Ren
Hate speech classifiers trained on imbalanced datasets struggle to determine if group identifiers like "gay" or "black" are used in offensive or prejudiced ways.
1 code implementation • IJCNLP 2019 • Aida Mostafazadeh Davani, Leigh Yeh, Mohammad Atari, Brendan Kennedy, Gwenyth Portillo-Wightman, Elaine Gonzalez, Natalie Delong, Rhea Bhatia, Arineh Mirinjian, Xiang Ren, Morteza Dehghani
Official reports of hate crimes in the US are under-reported relative to the actual number of such incidents.