Universal Dependencies for Learner English

Abstract

We introduce the Treebank of Learner English (TLE), the first publicly available syntactic treebank for English as a Second Language (ESL). The TLE provides manually annotated POS tags and Universal Dependency (UD) trees for 5,124 sentences from the Cambridge First Certificate in English (FCE) corpus. The UD annotations are tied to a pre-existing error annotation of the FCE, whereby full syntactic analyses are provided for both the original and error corrected versions of each sentence. Further on, we delineate ESL annotation guidelines that allow for consistent syntactic treatment of ungrammatical English. Finally, we benchmark POS tagging and dependency parsing performance on the TLE dataset and measure the effect of grammatical errors on parsing accuracy. We envision the treebank to support a wide range of linguistic and computational research on second language acquisition as well as automatic processing of ungrammatical language 1 .

Diversifying language models for lesser-studied languages and language-usage contexts: A case of second language Korean

“…Although previous studies have consistently shown that fine-tuning L1 pre-trained models with L2 data improves the accuracy of both tokenization and POS tagging for L2 data (Berzak et al, 2016;Kyle et al, 2022;Sung and Shin, 2023), there are two key questions unresolved with respect to developing L2 domain-specific models. First, it is unclear how the models perform in zero-shot scenarios with unseen L2 data (i.e., L2 test sets not sourced from the same origin as L2 training data 1 ), which is a crucial factor for enhancing the model's reliability and robustness (Choi and Palmer, 2012).…”