Abstract
In this paper, we introduce a dependency treebank of spoken second language (L2) English that is annotated with part of speech (Penn POS) tags and syntactic dependencies (Universal Dependencies). We then evaluate the degree to which the use of this treebank as training data affects POS and UD annotation accuracy for L1 web texts, L2 written texts, and L2 spoken texts as compared to models trained on L1 texts only.
Cite
CITATION STYLE
Kyle, K., Eguchi, M., Miller, A., & Sither, T. (2022). A Dependency Treebank of Spoken Second Language English. In BEA 2022 - 17th Workshop on Innovative Use of NLP for Building Educational Applications, Proceedings (pp. 39–45). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2022.bea-1.7
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