Gender-Distinguishing Features in Film Dialogue

By Alexandra Schofield, L. Mehr. 2016

23 citations recorded by the source corpus.

Film scripts provide a means of examining generalized western social perceptions of accepted human behavior. In particular, we focus on how dialogue in films describes gender, identifying linguistic and structural differences in speech for men and women and in same and different-gendered pairs. Using the Cornell Movie-Dialogs Corpus (DanescuNiculescu-Mizil et al., 2012a), we identify significant linguistic and structural features of dialogue that differentiate genders in conversation and analyze how those effects relate to existing literature on gender in film.

Subfields: Artificial Intelligence, Gender Studies, AI Ethics, Fairness & Society, Human-Computer Interaction

How researchers see this paper

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

In Artificial Intelligence

1. Yejin Choi
2. Rowan Zellers
3. Mohit Bansal
4. Ali Farhadi
5. Yonatan Bisk
6. Luke S. Zettlemoyer
7. Noah A. Smith
8. Jianfeng Gao

In Gender Studies

1. Yejin Choi

In AI Ethics, Fairness & Society

1. Ali Farhadi
2. Yejin Choi
3. Rowan Zellers

In Human-Computer Interaction

1. Ali Farhadi
2. Yejin Choi
3. Louis-philippe Morency

Citations in the corpus

• SWAG: A Large-Scale Adversarial Dataset for Grounded Commonsense Inference (2018, background): “However, it is not perfect in this regard, particularly due to biases in movies (Schofield and Mehr, 2016; Sap et al., 2017).”
• From Recognition to Cognition: Visual Commonsense Reasoning (2018, background): “21This line of work is complementary to other notions of dataset bias, like understanding what phenomena datasets cover or don’t [76], particularly how that relates to how marginalized groups are represented and portrayed [71, 90, 69, 68].”