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Improving textual network embedding with global attention via optimal transport

Publication ,  Conference
Chen, L; Wang, G; Tao, C; Shen, D; Cheng, P; Zhang, X; Wang, W; Zhang, Y; Carin, L
Published in: ACL 2019 - 57th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference
January 1, 2020

Constituting highly informative network embeddings is an important tool for network analysis. It encodes network topology, along with other useful side information, into low-dimensional node-based feature representations that can be exploited by statistical modeling. This work focuses on learning context-aware network embeddings augmented with text data. We reformulate the network-embedding problem, and present two novel strategies to improve over traditional attention mechanisms: (i) a content-aware sparse attention module based on optimal transport, and (ii) a high-level attention parsing module. Our approach yields naturally sparse and self-normalized relational inference. It can capture long-term interactions between sequences, thus addressing the challenges faced by existing textual network embedding schemes. Extensive experiments are conducted to demonstrate our model can consistently outperform alternative state-of-the-art methods.

Duke Scholars

Published In

ACL 2019 - 57th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference

ISBN

9781950737482

Publication Date

January 1, 2020

Start / End Page

5193 / 5202
 

Citation

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Chen, L., Wang, G., Tao, C., Shen, D., Cheng, P., Zhang, X., … Carin, L. (2020). Improving textual network embedding with global attention via optimal transport. In ACL 2019 - 57th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (pp. 5193–5202).
Chen, L., G. Wang, C. Tao, D. Shen, P. Cheng, X. Zhang, W. Wang, Y. Zhang, and L. Carin. “Improving textual network embedding with global attention via optimal transport.” In ACL 2019 - 57th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference, 5193–5202, 2020.
Chen L, Wang G, Tao C, Shen D, Cheng P, Zhang X, et al. Improving textual network embedding with global attention via optimal transport. In: ACL 2019 - 57th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference. 2020. p. 5193–202.
Chen, L., et al. “Improving textual network embedding with global attention via optimal transport.” ACL 2019 - 57th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference, 2020, pp. 5193–202.
Chen L, Wang G, Tao C, Shen D, Cheng P, Zhang X, Wang W, Zhang Y, Carin L. Improving textual network embedding with global attention via optimal transport. ACL 2019 - 57th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference. 2020. p. 5193–5202.

Published In

ACL 2019 - 57th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference

ISBN

9781950737482

Publication Date

January 1, 2020

Start / End Page

5193 / 5202