Assessing Grammatical Correctness in Language Learning

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review


We present experiments on assessing the grammatical correctness of learners’ answers in a language-learning System (references to the System, and the links to the released data and code are withheld for anonymity). In particular, we explore the problem of detecting alternative-correct answers: when more than one inflected form of a lemma fits syntactically and semantically in a given context. We approach the problem with the methods for grammatical error detection (GED), since we hypothesize that models for detecting grammatical mistakes can assess the correctness of potential alternative answers in a learning setting. Due to the paucity of training data, we explore the ability of pre-trained BERT to detect grammatical errors and then fine-tune it using synthetic training data. In this work, we focus on errors in inflection. Our experiments show a. that pre-trained BERT performs worse at detecting grammatical irregularities for Russian than for English; b. that fine-tuned BERT yields promising results on assessing the correctness of grammatical exercises; and c. establish a new benchmark for Russian. To further investigate its performance, we compare fine-tuned BERT with one of the state-of-the-art models for GED (Bell et al., 2019) on our dataset and RULEC-GEC (Rozovskaya and Roth, 2019). We release the manually annotated learner dataset, used for testing, for general use.
Original languageEnglish
Title of host publicationProceedings of the 16th Workshop on Innovative Use of NLP for Building Educational Applications
Number of pages12
Place of PublicationStroudsburg
PublisherThe Association for Computational Linguistics
Publication dateApr 2021
ISBN (Electronic)9781954085114
Publication statusPublished - Apr 2021
MoE publication typeA4 Article in conference proceedings
Event16th Workshop on Innovative Use of NLP for Building Educational Applications - Online
Duration: 20 Apr 202120 Apr 2021

Fields of Science

  • 113 Computer and information sciences

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