@inproceedings{moeller-arppe-2024-machine,
title = "Machine-in-the-Loop with Documentary and Descriptive Linguists",
author = "Moeller, Sarah and
Arppe, Antti",
editor = "Moeller, Sarah and
Agyapong, Godfred and
Arppe, Antti and
Chaudhary, Aditi and
Rijhwani, Shruti and
Cox, Christopher and
Henke, Ryan and
Palmer, Alexis and
Rosenblum, Daisy and
Schwartz, Lane",
booktitle = "Proceedings of the Seventh Workshop on the Use of Computational Methods in the Study of Endangered Languages",
month = mar,
year = "2024",
address = "St. Julians, Malta",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.computel-1.5",
pages = "27--32",
abstract = "This paper describes a curriculum for teaching linguists how to apply machine-in-the-loop (MitL) approach to documentary and descriptive tasks. It also shares observations about the learning participants, who are primarily non-computational linguists, and how they interact with the MitL approach. We found that they prefer cleaning over increasing the training data and then proceed to reanalyze their analytical decisions, before finally undertaking small actions that emphasize analytical strategies. Overall, participants display an understanding of the curriculum which covers fundamental concepts of machine learning and statistical modeling.",
}
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%0 Conference Proceedings
%T Machine-in-the-Loop with Documentary and Descriptive Linguists
%A Moeller, Sarah
%A Arppe, Antti
%Y Moeller, Sarah
%Y Agyapong, Godfred
%Y Arppe, Antti
%Y Chaudhary, Aditi
%Y Rijhwani, Shruti
%Y Cox, Christopher
%Y Henke, Ryan
%Y Palmer, Alexis
%Y Rosenblum, Daisy
%Y Schwartz, Lane
%S Proceedings of the Seventh Workshop on the Use of Computational Methods in the Study of Endangered Languages
%D 2024
%8 March
%I Association for Computational Linguistics
%C St. Julians, Malta
%F moeller-arppe-2024-machine
%X This paper describes a curriculum for teaching linguists how to apply machine-in-the-loop (MitL) approach to documentary and descriptive tasks. It also shares observations about the learning participants, who are primarily non-computational linguists, and how they interact with the MitL approach. We found that they prefer cleaning over increasing the training data and then proceed to reanalyze their analytical decisions, before finally undertaking small actions that emphasize analytical strategies. Overall, participants display an understanding of the curriculum which covers fundamental concepts of machine learning and statistical modeling.
%U https://aclanthology.org/2024.computel-1.5
%P 27-32
Markdown (Informal)
[Machine-in-the-Loop with Documentary and Descriptive Linguists](https://aclanthology.org/2024.computel-1.5) (Moeller & Arppe, ComputEL-WS 2024)
ACL