Daniel Buschek
2024
User-Centered Design of Digital Tools for Sociolinguistic Studies in Under-Resourced Languages
Jonas Adler
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Carsten Scholle
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Daniel Buschek
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Nicolo’ Brandizzi
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Muhadj Adnan
Proceedings of the 3rd Workshop on NLP Applications to Field Linguistics (Field Matters 2024)
Investigating language variation is a core aspect of sociolinguistics, especially through the use of linguistic corpora. Collecting and analyzing spoken language in text-based corpora can be time-consuming and error-prone, especially for under-resourced languages with limited software assistance. This paper explores the language variation research process using a User-Centered Design (UCD) approach from the field of Human-Computer Interaction (HCI), offering guidelines for the development of digital tools for sociolinguists. We interviewed four researchers, observed their workflows and software usage, and analyzed the data using Grounded Theory. This revealed key challenges in manual tasks, software assistance, and data management. Based on these insights, we identified a set of requirements that future tools should meet to be valuable for researchers in this domain. The paper concludes by proposing design concepts with sketches and prototypes based on the identified requirements. These concepts aim to guide the implementation of a fully functional, open-source tool. This work presents an interdisciplinary approach between sociolinguistics and HCI by emphasizing the practical aspects of research that are often overlooked.
2021
Methods for the Design and Evaluation of HCI+NLP Systems
Hendrik Heuer
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Daniel Buschek
Proceedings of the First Workshop on Bridging Human–Computer Interaction and Natural Language Processing
HCI and NLP traditionally focus on different evaluation methods. While HCI involves a small number of people directly and deeply, NLP traditionally relies on standardized benchmark evaluations that involve a larger number of people indirectly. We present five methodological proposals at the intersection of HCI and NLP and situate them in the context of ML-based NLP models. Our goal is to foster interdisciplinary collaboration and progress in both fields by emphasizing what the fields can learn from each other.
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