Classifying Syntactic Errors in Learner Language
We present a method for classifying syntactic errors in learner language, namely errors whose correction alters the morphosyntactic structure of a sentence. The methodology builds on the established Universal Dependencies syntactic representation scheme, and provides complementary information to other error-classification systems. Unlike existing error classification methods, our method is applicable across languages, which we showcase by producing a detailed picture of syntactic errors in learner English and learner Russian. We further demonstrate the utility of the methodology for analyzing the outputs of leading Grammatical Error Correction (GEC) systems.
Code (1)
Tasks
ClassificationGeneral ClassificationGrammatical Error CorrectionSentenceSimilar Papers 제목 키워드 기반
Automatically Detecting Syntactic Errors in Sentences Writing by Learners of Chinese as a Foreign Language
Syntactic Well-Formedness Diagnosis and Error-Based Coaching in Computer Assisted Language Learning using Machine Translation
We present a novel approach to Computer Assisted Language Learning (CALL), using deep syntactic parsers and semantic based machine translation (MT) in diagnosing and providing explicit feedback on language learners{'} er…
Machine TranslationTranslationUniversal Dependencies for Learner English
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…
Dependency ParsingLanguage AcquisitionPOSPOS Tagging+1Predicting proficiency levels in learner writings by transferring a linguistic complexity model from expert-written coursebooks
The lack of a sufficient amount of data tailored for a task is a well-recognized problem for many statistical NLP methods. In this paper, we explore whether data sparsity can be successfully tackled when classifying lang…
Domain AdaptationLanguage AcquisitionTransfer LearningSyntactic Structure Distillation Pretraining For Bidirectional Encoders
Textual representation learners trained on large amounts of data have achieved notable success on downstream tasks; intriguingly, they have also performed well on challenging tests of syntactic competence. Given this suc…
Knowledge DistillationLanguage ModelingLanguage ModellingNatural Language Understanding+2