Abstract
L1-L2 parallel dependency treebanks are UD-annotated corpora of learner sentences paired with correction hypotheses. Automatic morphosyntactical annotation has the potential to remove the need for explicit manual error tagging and improve interoperability, but makes it more challenging to locate grammatical errors in the resulting datasets. We therefore propose a novel method for automatically extracting morphosyntactical error patterns and perform a preliminary bilingual evaluation of its first implementation through a similar example retrieval task. The resulting pipeline is also available as a prototype CALL application.- Anthology ID:
- 2023.bea-1.50
- Volume:
- Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2023)
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Editors:
- Ekaterina Kochmar, Jill Burstein, Andrea Horbach, Ronja Laarmann-Quante, Nitin Madnani, Anaïs Tack, Victoria Yaneva, Zheng Yuan, Torsten Zesch
- Venue:
- BEA
- SIG:
- SIGEDU
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 585–597
- Language:
- URL:
- https://aclanthology.org/2023.bea-1.50
- DOI:
- 10.18653/v1/2023.bea-1.50
- Cite (ACL):
- Arianna Masciolini, Elena Volodina, and Dana Dannlls. 2023. Towards automatically extracting morphosyntactical error patterns from L1-L2 parallel dependency treebanks. In Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2023), pages 585–597, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Towards automatically extracting morphosyntactical error patterns from L1-L2 parallel dependency treebanks (Masciolini et al., BEA 2023)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/2023.bea-1.50.pdf