Error syntax aware augmentation of feedback comment generation dataset

Nikolay Babakov, Maria Lysyuk, Alexander Shvets, Lilya Kazakova, Alexander Panchenko


Abstract
This paper presents a solution to the GenChal 2022 shared task dedicated to feedback comment generation for writing learning. In terms of this task given a text with an error and a span of the error, a system generates an explanatory note that helps the writer (language learner) to improve their writing skills. Our solution is based on fine-tuning the T5 model on the initial dataset augmented according to syntactical dependencies of the words located within indicated error span. The solution of our team ‘nigula’ obtained second place according to manual evaluation by the organizers.
Anthology ID:
2023.inlg-genchal.6
Volume:
Proceedings of the 16th International Natural Language Generation Conference: Generation Challenges
Month:
September
Year:
2023
Address:
Prague, Czechia
Editor:
Simon Mille
Venues:
INLG | SIGDIAL
SIG:
SIGGEN
Publisher:
Association for Computational Linguistics
Note:
Pages:
37–44
Language:
URL:
https://aclanthology.org/2023.inlg-genchal.6
DOI:
Bibkey:
Cite (ACL):
Nikolay Babakov, Maria Lysyuk, Alexander Shvets, Lilya Kazakova, and Alexander Panchenko. 2023. Error syntax aware augmentation of feedback comment generation dataset. In Proceedings of the 16th International Natural Language Generation Conference: Generation Challenges, pages 37–44, Prague, Czechia. Association for Computational Linguistics.
Cite (Informal):
Error syntax aware augmentation of feedback comment generation dataset (Babakov et al., INLG-SIGDIAL 2023)
Copy Citation:
PDF:
https://preview.aclanthology.org/nschneid-patch-1/2023.inlg-genchal.6.pdf