Accurate, Diverse and Multiple Distractor Generation with Mixture of Experts-Reference-Cited by-同舟云学术

Accurate, Diverse and Multiple Distractor Generation with Mixture of Experts

Published:2023 Issue: Volume: Page:761-773
ISSN:0302-9743
Container-title:Natural Language Processing and Chinese Computing
language:
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Author:

Qu Fanyi,Wang Che,Wu Yunfang

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-44693-1_59

Reference19 articles.

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2. Cho, J., Seo, M.J., Hajishirzi, H.: Mixture content selection for diverse sequence generation. In: Inui, K., Jiang, J., Ng, V., Wan, X. (eds.) Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, EMNLP-IJCNLP 2019, Hong Kong, China, 3–7 November 2019, pp. 3119–3129. Association for Computational Linguistics (2019)

3. Dai, D., et al.: StableMoE: stable routing strategy for mixture of experts. In: Muresan, S., Nakov, P., Villavicencio, A. (eds.) Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACL 2022, Dublin, Ireland, 22–27 May 2022, pp. 7085–7095. Association for Computational Linguistics (2022)

4. Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the EM algorithm. J. Roy. Stat. Soc.: Ser. B (Methodol.) 39(1), 1–22 (1977)

5. Fan, A., Lewis, M., Dauphin, Y.: Hierarchical neural story generation. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 889–898. Association for Computational Linguistics, Melbourne, Australia, July 2018