Advancements in Arabic Grammatical Error Detection and Correction: An Empirical Investigation
Document Type
Conference Proceeding
Publication Title
EMNLP 2023 - 2023 Conference on Empirical Methods in Natural Language Processing, Proceedings
Abstract
Grammatical error correction (GEC) is a well-explored problem in English with many existing models and datasets. However, research on GEC in morphologically rich languages has been limited due to challenges such as data scarcity and language complexity. In this paper, we present the first results on Arabic GEC using two newly developed Transformer-based pretrained sequence-to-sequence models. We also define the task of multi-class Arabic grammatical error detection (GED) and present the first results on multi-class Arabic GED. We show that using GED information as an auxiliary input in GEC models improves GEC performance across three datasets spanning different genres. Moreover, we also investigate the use of contextual morphological preprocessing in aiding GEC systems. Our models achieve SOTA results on two Arabic GEC shared task datasets and establish a strong benchmark on a recently created dataset. We make our code, data, and pretrained models publicly available.
First Page
6430
Last Page
6448
Publication Date
1-1-2023
Recommended Citation
B. Alhafni et al., "Advancements in Arabic Grammatical Error Detection and Correction: An Empirical Investigation," EMNLP 2023 - 2023 Conference on Empirical Methods in Natural Language Processing, Proceedings, pp. 6430 - 6448, Jan 2023.