Haley Lepp

Also published as: Haley M. Lepp


2022

pdf bib
Proceedings of the Sixth Widening NLP Workshop (WiNLP)
Shaily Bhatt | Sunipa Dev | Bonaventure Dossou | Tirthankar Ghosal | Hatem Haddad | Haley M. Lepp | Fatemehsadat Mireshghallah | Surangika Ranathunga | Xanda Schofield | Isidora Tourni | Weijia Xu
Proceedings of the Sixth Widening NLP Workshop (WiNLP)

2021

pdf bib
Proceedings of the Fifth Workshop on Widening Natural Language Processing
Erika Varis | Ryan Georgi | Alicia Tsai | Antonios Anastasopoulos | Kyathi Chandu | Xanda Schofield | Surangika Ranathunga | Haley Lepp | Tirthankar Ghosal
Proceedings of the Fifth Workshop on Widening Natural Language Processing

2019

pdf bib
Visualizing Inferred Morphotactic Systems
Haley Lepp | Olga Zamaraeva | Emily M. Bender
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics (Demonstrations)

We present a web-based system that facilitates the exploration of complex morphological patterns found in morphologically very rich languages. The need for better understanding of such patterns is urgent for linguistics and important for cross-linguistically applicable natural language processing. In this paper we give an overview of the system architecture and describe a sample case study on Abui [abz], a Trans-New Guinea language spoken in Indonesia.

bib
Pardon the Interruption: Automatic Analysis of Gender and Competitive Turn-Taking in United States Supreme Court Hearings
Haley Lepp
Proceedings of the 2019 Workshop on Widening NLP

The United States Supreme Court plays a key role in defining the legal basis for gender discrimination throughout the country, yet there are few checks on gender bias within the court itself. In conversational turn-taking, interruptions have been documented as a marker of bias between speakers of different genders. The goal of this study is to automatically differentiate between respectful and disrespectful conversational turns taken during official hearings, which could help in detecting bias and finding remediation techniques for discourse in the courtroom. In this paper, I present a corpus of turns annotated by legal professionals, and describe the design of a semi-supervised classifier that will use acoustic and lexical features to analyze turn-taking at scale. On completion of annotations, this classifier will be trained to extract the likelihood that turns are respectful or disrespectful for use in studies of speech trends.