Can a Neural Model Guide Fieldwork? A Case Study on Morphological Data Collection
Abstract
Linguistic fieldwork is an important component in language documentation and preservation. However, it is a long, exhaustive, and time-consuming process. This paper presents a novel model that guides a linguist during the fieldwork and accounts for the dynamics of linguist-speaker interactions. We introduce a novel framework that evaluates the efficiency of various sampling strategies for obtaining morphological data and assesses the effectiveness of state-of-the-art neural models in generalising morphological structures. Our experiments highlight two key strategies for improving the efficiency: (1) increasing the diversity of annotated data by uniform sampling among the cells of the paradigm tables, and (2) using model confidence as a guide to enhance positive interaction by providing reliable predictions during annotation.
Cite
@article{arxiv.2409.14628,
title = {Can a Neural Model Guide Fieldwork? A Case Study on Morphological Data Collection},
author = {Aso Mahmudi and Borja Herce and Demian Inostroza Amestica and Andreas Scherbakov and Eduard Hovy and Ekaterina Vylomova},
journal= {arXiv preprint arXiv:2409.14628},
year = {2024}
}