SESAME - self-supervised framework for extractive question answering over document collections
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s10844-024-00869-6.pdf
Reference57 articles.
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2. Almazrouei, E., Alobeidli, H., & Alshamsi, A., et al. (2023). The falcon series of open language models. https://doi.org/10.48550/arXiv.2311.16867
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4. Assem, H., Sarkar, R., & Dutta, S. (2021). Qasar: Self-supervised learning framework for extractive question answering. In: 2021 IEEE international conference on big data (Big data) (pp. 1797–1808). https://doi.org/10.1109/BigData52589.2021.9671570
5. Banerjee, P., Gokhale, T., & Baral, C. (2021). Self-supervised test-time learning for reading comprehension. In: K. Toutanova, A. Rumshisky, & L. Zettlemoyer, et al (Eds.), Proceedings of the 2021 conference of the North American chapter of the association for computational linguistics: human language technologies (pp. 1200–1211). Association for Computational Linguistics. https://doi.org/10.18653/v1/2021.naacl-main.95
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