@inproceedings{mayfield-etal-2019-equity,
title = "Equity Beyond Bias in Language Technologies for Education",
author = "Mayfield, Elijah and
Madaio, Michael and
Prabhumoye, Shrimai and
Gerritsen, David and
McLaughlin, Brittany and
Dixon-Rom{\'a}n, Ezekiel and
Black, Alan W",
editor = "Yannakoudakis, Helen and
Kochmar, Ekaterina and
Leacock, Claudia and
Madnani, Nitin and
Pil{\'a}n, Ildik{\'o} and
Zesch, Torsten",
booktitle = "Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications",
month = aug,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-4446",
doi = "10.18653/v1/W19-4446",
pages = "444--460",
abstract = "There is a long record of research on equity in schools. As machine learning researchers begin to study fairness and bias in earnest, language technologies in education have an unusually strong theoretical and applied foundation to build on. Here, we introduce concepts from culturally relevant pedagogy and other frameworks for teaching and learning, identifying future work on equity in NLP. We present case studies in a range of topics like intelligent tutoring systems, computer-assisted language learning, automated essay scoring, and sentiment analysis in classrooms, and provide an actionable agenda for research.",
}
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<abstract>There is a long record of research on equity in schools. As machine learning researchers begin to study fairness and bias in earnest, language technologies in education have an unusually strong theoretical and applied foundation to build on. Here, we introduce concepts from culturally relevant pedagogy and other frameworks for teaching and learning, identifying future work on equity in NLP. We present case studies in a range of topics like intelligent tutoring systems, computer-assisted language learning, automated essay scoring, and sentiment analysis in classrooms, and provide an actionable agenda for research.</abstract>
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%0 Conference Proceedings
%T Equity Beyond Bias in Language Technologies for Education
%A Mayfield, Elijah
%A Madaio, Michael
%A Prabhumoye, Shrimai
%A Gerritsen, David
%A McLaughlin, Brittany
%A Dixon-Román, Ezekiel
%A Black, Alan W.
%Y Yannakoudakis, Helen
%Y Kochmar, Ekaterina
%Y Leacock, Claudia
%Y Madnani, Nitin
%Y Pilán, Ildikó
%Y Zesch, Torsten
%S Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F mayfield-etal-2019-equity
%X There is a long record of research on equity in schools. As machine learning researchers begin to study fairness and bias in earnest, language technologies in education have an unusually strong theoretical and applied foundation to build on. Here, we introduce concepts from culturally relevant pedagogy and other frameworks for teaching and learning, identifying future work on equity in NLP. We present case studies in a range of topics like intelligent tutoring systems, computer-assisted language learning, automated essay scoring, and sentiment analysis in classrooms, and provide an actionable agenda for research.
%R 10.18653/v1/W19-4446
%U https://aclanthology.org/W19-4446
%U https://doi.org/10.18653/v1/W19-4446
%P 444-460
Markdown (Informal)
[Equity Beyond Bias in Language Technologies for Education](https://aclanthology.org/W19-4446) (Mayfield et al., BEA 2019)
ACL
- Elijah Mayfield, Michael Madaio, Shrimai Prabhumoye, David Gerritsen, Brittany McLaughlin, Ezekiel Dixon-Román, and Alan W Black. 2019. Equity Beyond Bias in Language Technologies for Education. In Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications, pages 444–460, Florence, Italy. Association for Computational Linguistics.