Papers by Zeerak Waseem
“Hold on honey, men at work”: A semi-supervised approach to detecting sexism in sitcoms (2021.acl-srw)
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| Challenge: | sexist dialogue in sitcoms is an important part of society's development, according to Sink and Mastro (2017). |
| Approach: | They propose a semi-supervised text classification model that automatically detects instances of sexism in popular sitcom dialogues. |
| Outcome: | The proposed model outperforms deep learning-based systems in detecting sexist dialogues over time and shows that sexism decreases over the years. |
HateCheck: Functional Tests for Hate Speech Detection Models (2021.acl-long)
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| Challenge: | Hate speech detection models are evaluated by measuring their performance on held-out test data using metrics such as accuracy and F1 score. |
| Approach: | They propose a suite of functional tests for hate speech detection models that measure model performance on held-out test data and then craft test cases to validate their quality. |
| Outcome: | The proposed tests show that the proposed models perform poorly on a small set of widely-used hate speech datasets. |
A Survey of Race, Racism, and Anti-Racism in NLP (2021.acl-long)
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| Challenge: | despite inextricable ties between race and language, little work has considered race in NLP research and development. |
| Approach: | They survey 79 papers from the ACL anthology that mention race . they find race has been siloed as a niche topic and ignored in many NLP tasks . authors call for inclusion and racial justice in NLP research practices . |
| Outcome: | The findings highlight the need for inclusion and racial justice in NLP research practices. |
Dynabench: Rethinking Benchmarking in NLP (2021.naacl-main)
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Douwe Kiela, Max Bartolo, Yixin Nie, Divyansh Kaushik, Atticus Geiger, Zhengxuan Wu, Bertie Vidgen, Grusha Prasad, Amanpreet Singh, Pratik Ringshia, Zhiyi Ma, Tristan Thrush, Sebastian Riedel, Zeerak Waseem, Pontus Stenetorp, Robin Jia, Mohit Bansal, Christopher Potts, Adina Williams
| Challenge: | Dynabench is an open-source platform for dynamic dataset creation and model benchmarking. |
| Approach: | They propose an open-source platform for dynamic dataset creation and model benchmarking. |
| Outcome: | The proposed platform can be used to create models that fail on simple challenges and falter in real-world scenarios. |
Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection (2021.acl-long)
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| Challenge: | Detecting online hate speech has proven difficult and concerns raised about performance, robustness, generalisability and fairness of stateof-the-art models. |
| Approach: | They propose a human-and-model-in-the-loop process for dynamically generating datasets and training better performing hate detection models. |
| Outcome: | The proposed model improves on a dataset of 40,000 hateful entries . the model is harder for annotators to trick and better on HateCheck . |