A Dependency Treebank of Spoken Second Language English
Kristopher Kyle | Masaki Eguchi | Aaron Miller | Theodore Sither
Proceedings of the 17th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2022)
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.