niksss at SemEval-2022 Task7:Transformers for Grading the Clarifications on Instructional Texts

Nikhil Singh


Abstract
This paper describes the 9th place system description for SemEval-2022 Task 7. The goal of this shared task was to develop computational models to predict how plausible a clarification made on an instructional text is. This shared task was divided into two Subtasks A and B. We attempted to solve these using various transformers-based architecture under different regime. We initially treated this as a text2text generation problem but comparing it with our recent approach we dropped it and treated this as a text-sequence classification and regression depending on the Subtask.
Anthology ID:
2022.semeval-1.154
Volume:
Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)
Month:
July
Year:
2022
Address:
Seattle, United States
Venue:
SemEval
SIGs:
SIGLEX | SIGSEM
Publisher:
Association for Computational Linguistics
Note:
Pages:
1090–1093
Language:
URL:
https://aclanthology.org/2022.semeval-1.154
DOI:
10.18653/v1/2022.semeval-1.154
Bibkey:
Cite (ACL):
Nikhil Singh. 2022. niksss at SemEval-2022 Task7:Transformers for Grading the Clarifications on Instructional Texts. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), pages 1090–1093, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
niksss at SemEval-2022 Task7:Transformers for Grading the Clarifications on Instructional Texts (Singh, SemEval 2022)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingestion-script-update/2022.semeval-1.154.pdf