UoR at SemEval-2021 Task 4: Using Pre-trained BERT Token Embeddings for Question Answering of Abstract Meaning

Thanet Markchom, Huizhi Liang


Abstract
Most question answering tasks focuses on predicting concrete answers, e.g., named entities. These tasks can be normally achieved by understanding the contexts without additional information required. In Reading Comprehension of Abstract Meaning (ReCAM) task, the abstract answers are introduced. To understand abstract meanings in the context, additional knowledge is essential. In this paper, we propose an approach that leverages the pre-trained BERT Token embeddings as a prior knowledge resource. According to the results, our approach using the pre-trained BERT outperformed the baselines. It shows that the pre-trained BERT token embeddings can be used as additional knowledge for understanding abstract meanings in question answering.
Anthology ID:
2021.semeval-1.106
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:
799–804
Language:
URL:
https://aclanthology.org/2021.semeval-1.106
DOI:
10.18653/v1/2021.semeval-1.106
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2021.semeval-1.106.pdf
Data
ReCAM