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

arXiv: https://arxiv.org/abs/2409.14628 · HTML: https://mlsanity.com/paper/2409.14628/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 33 references that are in the map.
- First posted on arXiv 22 Sep 2024.

## Builds on (most cited first, 9 of 9)

- [A Survey of Deep Active Learning](https://mlsanity.com/paper/2009.00236/) (2020)
- [The SIGMORPHON 2019 Shared Task: Morphological Analysis in Context and Cross-Lingual Transfer for Inflection](https://mlsanity.com/paper/1910.11493/) (2019)
- [Applying the Transformer to Character-level Transduction](https://mlsanity.com/paper/2005.10213/) (2020)
- [The CoNLL--SIGMORPHON 2018 Shared Task: Universal Morphological Reinflection](https://mlsanity.com/paper/1810.07125/) (2018)
- [CoNLL-SIGMORPHON 2017 Shared Task: Universal Morphological Reinflection in 52 Languages](https://mlsanity.com/paper/1706.09031/) (2017)
- [SIGMORPHON 2020 Shared Task 0: Typologically Diverse Morphological Inflection](https://mlsanity.com/paper/2006.11572/) (2020)
- [The SIGMORPHON 2022 Shared Task on Morpheme Segmentation](https://mlsanity.com/paper/2206.07615/) (2022)
- [The Paradigm Discovery Problem](https://mlsanity.com/paper/2005.01630/) (2020)
- [Eeny, meeny, miny, moe. How to choose data for morphological inflection](https://mlsanity.com/paper/2210.14465/) (2022)

## Built on by (most cited first, 1 of 1)

- [CommonMorph: Participatory Morphological Documentation Platform](https://mlsanity.com/paper/2604.04515/) (2026)

## Abstract (arXiv)

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.