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

TL;DR

This paper introduces a neural model to assist linguists in morphological data collection during fieldwork, improving efficiency through strategic sampling and model confidence guidance, thereby streamlining language documentation efforts.

Contribution

It presents a novel framework for evaluating sampling strategies and neural model effectiveness in morphological data collection, integrating linguist-model interaction dynamics.

Findings

Uniform sampling increases data diversity.

Model confidence guides more effective annotation.

Enhanced interaction improves morphological data collection efficiency.

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.