Learning and Understanding Different Categories of Sexism Using Convolutional Neural Network’s Filters

By Sima Sharifirad, Alon Jacovi. 2019

22 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Douwe Kiela
2. Emily Dinan
3. Kurt Shuster
4. Jason Aaron Edward Weston
5. Eric Michael Smith
6. Adina Williams
7. Mohit Bansal
8. Angela Fan

Citations in the corpus

• Queens Are Powerful Too: Mitigating Gender Bias in Dialogue Generation (2019, neutral): “…et al. 2019; Garimella et al. 2019; Gaut et al. 2020; Habash et al. 2019; Hashempour 2019; Hoyle et al. 2019; Lee et al. 2019a; Lepp 2019; Qian 2019; Qian et al. 2019; Sharifirad et al. 2019; Sharifirad and Matwin 2019; Stanovsky et al. 2019; O’Neil 2016; Blodgett et al. 2020; Nangia et al. 2020).”
• Multi-Dimensional Gender Bias Classification (2020, background): “2020), identification of offensive content (Sharifirad and Matwin, 2019; Sharifirad et al., 2019), and machine translation (Stanovsky et al.”
• Misogyny Detection in Twitter: a Multilingual and Cross-Domain Study (2020, unknown): “A more recent study by Sharifirad and Jacovi (2019) presented a new categorization of sexism including indirect, sexual, and physical sexism, building a CNN model to automatic classify tweets into these three categories.”
• Theories of “Gender” in NLP Bias Research (2022, background): “[130] Sima Sharifirad, Alon Jacovi, and Stan Matwin.”