Exploring highly concise and accurate text matching model with tiny weights
Author:
Funder
National Key R &D Program of China
Major Programs of the National Social Science Foundation of China
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s11280-024-01262-7.pdf
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3. Bowman, S.R., Angeli, G., Potts, C., et al.: A large annotated corpus for learning natural language inference. In: Màrquez L, Callison-Burch C, Su J, et al (eds.) Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, EMNLP 2015, Lisbon, Portugal, September 17-21, 2015. The Association for Computational Linguistics, pp. 632–642 (2015b). https://doi.org/10.18653/v1/d15-1075
4. Chen, Q., Zhu, X., Ling, Z.H., et al.: Enhanced LSTM for natural language inference. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, Vancouver, Canada, pp. 1657–1668 (2017). https://doi.org/10.18653/v1/P17-1152. https://aclanthology.org/P17-1152
5. Ding, H., Chen, K., Huo, Q.: Compressing cnn-dblstm models for ocr with teacher-student learning and tucker decomposition. Pattern Recogn. 96, (2019). https://doi.org/10.1016/j.patcog.2019.07.002. https://www.sciencedirect.com/science/article/pii/S0031320319302547
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