Semantic Answer Type Prediction using BERT: IAI at the ISWC SMART Task 2020
Abstract
This paper summarizes our participation in the SMART Task of the ISWC 2020 Challenge. A particular question we are interested in answering is how well neural methods, and specifically transformer models, such as BERT, perform on the answer type prediction task compared to traditional approaches. Our main finding is that coarse-grained answer types can be identified effectively with standard text classification methods, with over 95% accuracy, and BERT can bring only marginal improvements. For fine-grained type detection, on the other hand, BERT clearly outperforms previous retrieval-based approaches.
Cite
@article{arxiv.2109.06714,
title = {Semantic Answer Type Prediction using BERT: IAI at the ISWC SMART Task 2020},
author = {Vinay Setty and Krisztian Balog},
journal= {arXiv preprint arXiv:2109.06714},
year = {2021}
}
Comments
Published in Proceedings of the SeMantic AnsweR Type prediction task (SMART) at ISWC 2020 Semantic Web Challenge co-located with the 19th International Semantic Web Conference (ISWC 2020). http://ceur-ws.org/Vol-2774/paper-02.pdf