Early Guessing for Dialect Identification
Vani Kanjirangat, Tanja Samardzic, Fabio Rinaldi, Ljiljana Dolamic
Abstract
This paper deals with the problem of incre-mental dialect identification. Our goal is toreliably determine the dialect before the fullutterance is given as input. The major partof the previous research on dialect identification has been model-centric, focusing on performance. We address a new question: How much input is needed to identify a dialect? Ourapproach is a data-centric analysis that resultsin general criteria for finding the shortest inputneeded to make a plausible guess. Workingwith three sets of language dialects (Swiss German, Indo-Aryan and Arabic languages), weshow that it is possible to generalize across dialects and datasets with two input shorteningcriteria: model confidence and minimal inputlength (adjusted for the input type). The sourcecode for experimental analysis can be found atGithub.
BibTeX
@inproceedings{kanjirangat-etal-2022-early,
title = "Early Guessing for Dialect Identification",
author = "Kanjirangat, Vani and
Samardzic, Tanja and
Rinaldi, Fabio and
Dolamic, Ljiljana",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-emnlp.479/",
doi = "10.18653/v1/2022.findings-emnlp.479",
pages = "6417--6426"
}