Assessing Grammatical Correctness in Language Learning

Anisia Katinskaia, Roman Yangarber


Abstract
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.
Anthology ID:
2021.bea-1.15
Volume:
Proceedings of the 16th Workshop on Innovative Use of NLP for Building Educational Applications
Month:
April
Year:
2021
Address:
Online
Venue:
BEA
SIG:
SIGEDU
Publisher:
Association for Computational Linguistics
Note:
Pages:
135–146
Language:
URL:
https://aclanthology.org/2021.bea-1.15
DOI:
Bibkey:
Cite (ACL):
Anisia Katinskaia and Roman Yangarber. 2021. Assessing Grammatical Correctness in Language Learning. In Proceedings of the 16th Workshop on Innovative Use of NLP for Building Educational Applications, pages 135–146, Online. Association for Computational Linguistics.
Cite (Informal):
Assessing Grammatical Correctness in Language Learning (Katinskaia & Yangarber, BEA 2021)
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PDF:
https://aclanthology.org/2021.bea-1.15.pdf