Universal Dependencies for Learner Russian
Abstract
We introduce a pilot annotation of Russian learner data with syntactic dependency relations. The annotation is performed on a subset of sentences from RULEC-GEC and RU-Lang8, two error-corrected Russian learner datasets. We provide manually labeled Universal Dependency (UD) trees for 500 sentence pairs, annotating both the original (source) and the corrected (target) version of each sentence. Further, we outline guidelines for annotating learner Russian data containing non-standard erroneous text and analyze the effect that the individual errors have on the resulting dependency trees. This study should contribute to a wide range of computational and theoretical research directions in second language learning and grammatical error correction.
BibTeX
@inproceedings{rozovskaya-2024-universal,
title = "{U}niversal {D}ependencies for Learner {R}ussian",
author = "Rozovskaya, Alla",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.1486/",
pages = "17112--17119"
}