KGP at SemEval-2021 Task 8: Leveraging Multi-Staged Language Models for Extracting Measurements, their Attributes and Relations

Neel Karia, Ayush Kaushal, Faraaz Mallick


Abstract
SemEval-2021 Task 8: MeasEval aims at improving the machine understanding of measurements in scientific texts through a set of entity and semantic relation extraction sub-tasks on identifying quantity spans along with various attributes and relationships. This paper describes our system, consisting of a three-stage pipeline, that leverages pre-trained language models to extract the quantity spans in the text, followed by intelligent templates to identify units and modifiers. Finally, it identifies the quantity attributes and their relations using language models boosted with a feature re-using hierarchical architecture and multi-task learning. Our submission significantly outperforms the baseline, with the best model from the post-evaluation phase delivering more than 100% increase on F1 (Overall) from the baseline.
Anthology ID:
2021.semeval-1.46
Volume:
Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)
Month:
August
Year:
2021
Address:
Online
Venues:
ACL | IJCNLP | SemEval
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
387–396
Language:
URL:
https://aclanthology.org/2021.semeval-1.46
DOI:
10.18653/v1/2021.semeval-1.46
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2021.semeval-1.46.pdf