An empirical study of pre-trained language models in simple knowledge graph question answering-Reference-Cited by-同舟云学术

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An empirical study of pre-trained language models in simple knowledge graph question answering

Published:2023-05-17 Issue:5 Volume:26 Page:2855-2886
ISSN:1386-145X
Container-title:World Wide Web
language:en
Short-container-title:World Wide Web

Author:

Hu Nan,Wu Yike,Qi Guilin,Min Dehai,Chen Jiaoyan,Pan Jeff Z,Ali Zafar

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Hardware and Architecture,Software

Link

https://link.springer.com/content/pdf/10.1007/s11280-023-01166-y.pdf

Reference65 articles.

1. Manning, C.D.: Human language understanding & reasoning. In: Daedalus, pp. 127–138 (2022). https://doi.org/10.1162/daed_a_01905

2. Mohammed, S., Shi, P., Lin, J.J.: Strong baselines for simple question answering over knowledge graphs with and without neural networks. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics, pp. 291–296 (2018). https://doi.org/10.18653/v1/n18-2047

3. Lukovnikov, D., Fischer, A., Lehmann, J.: Pretrained transformers for simple question answering over knowledge graphs. In: 18th International Semantic Web Conference, pp. 470–486 (2019). https://doi.org/10.1007/978-3-030-30793-6_27

4. Golub, D., He, X.: Character-level question answering with attention. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 1598–1607 (2016). https://doi.org/10.18653/v1/d16-1166

5. Petrochuk, M., Zettlemoyer, L.: Simple questions nearly solved: a new upperbound and baseline approach. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 554–558 (2018). https://doi.org/10.18653/v1/d18-1051

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