Can a Neural Model Guide Fieldwork? A Case Study on Morphological Data Collection

Aso Mahmudi, Borja Herce, Demian Inostroza Améstica, Andreas Scherbakov, Eduard H. Hovy, Ekaterina Vylomova

Abstract: Linguistic fieldwork is an important component in language documentation and the creation of comprehensive linguistic corpora. Despite its significance, the process is often lengthy, exhaustive, and time-consuming. 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.

External IDs:dblp:conf/coling/MahmudiHASHV25