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.- Anthology ID:
- 2022.findings-emnlp.479
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2022
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates
- Editors:
- Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 6417–6426
- Language:
- URL:
- https://aclanthology.org/2022.findings-emnlp.479
- DOI:
- 10.18653/v1/2022.findings-emnlp.479
- Cite (ACL):
- Vani Kanjirangat, Tanja Samardzic, Fabio Rinaldi, and Ljiljana Dolamic. 2022. Early Guessing for Dialect Identification. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 6417–6426, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
- Cite (Informal):
- Early Guessing for Dialect Identification (Kanjirangat et al., Findings 2022)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/2022.findings-emnlp.479.pdf