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UoR at SemEval-2021 task 4: using pre-trained BERT Token embeddings for question answering of abstract meaning

Markchom, T. and Liang, H. (2021) UoR at SemEval-2021 task 4: using pre-trained BERT Token embeddings for question answering of abstract meaning. In: SemEval-2021, 5-6 August 2021, Bangkok. (In Press)

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Abstract/Summary

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.

Item Type:Conference or Workshop Item (Paper)
Refereed:Yes
Divisions:Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:97211

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