Reading list

From word embedding to sentence embedding:  

1. Arora, S., Liang, Y., & Ma, T. (2017). A simple but tough-to-beat baseline for sentence embeddings. In International conference on learning representations.

Hierarchical Attention Network: 

Yang, Z., Yang, D., Dyer, C., He, X., Smola, A., & Hovy, E. (2016, June). Hierarchical attention networks for document classification. In Proceedings of the 2016 conference of the North American chapter of the association for computational linguistics: human language technologies (pp. 1480-1489).

Document Analysis (Extensions of Hierarchical Attention Network):

1. Cohan, A., Feldman, S., Beltagy, I., Downey, D., & Weld, D. S. (2020, July). SPECTER: Document-level Representation Learning using Citation-informed Transformers. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (pp. 2270-2282).

2. Liu, T., Hu, Y., Wang, B., Sun, Y., Gao, J., & Yin, B. (2022). Hierarchical graph convolutional networks for structured long document classification. IEEE transactions on neural networks and learning systems.

3. Hu, Y., Chen, P., Liu, T., Gao, J., Sun, Y., & Yin, B. (2021, July). Hierarchical attention transformer networks for long document classification. In 2021 International Joint Conference on Neural Networks (IJCNN) (pp. 1-7). IEEE.

4. Huang, Y., Chen, J., Zheng, S., Xue, Y., & Hu, X. (2021). Hierarchical multi-attention networks for document classification. International Journal of Machine Learning and Cybernetics,12, 1639-1647.