Pardon the Interruption: Automatic Analysis of Gender and Competitive Turn-Taking in United States Supreme Court Hearings

By Haley Lepp. 2019

3 citations recorded by the source corpus.

Subfields: Natural Language Processing, AI Ethics, Fairness & Society, Social Sciences & Humanities

How researchers see this paper

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

In Natural Language Processing

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

In AI Ethics, Fairness & Society

1. Douwe Kiela
2. Emily Dinan
3. Adina Williams

In Social Sciences & Humanities

1. Jason Aaron Edward Weston
2. Douwe Kiela
3. Emily Dinan
4. Adina Williams

Citations in the corpus

• Queens Are Powerful Too: Mitigating Gender Bias in Dialogue Generation (2019, background): “…Du et al. 2019; Emami 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.…”
• Multi-Dimensional Gender Bias Classification (2020, background): “propriate turn-taking classification (Lepp, 2019), relation extraction (Gaut et al.”