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
- 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)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2022.semeval-1.154.pdf