Improving Textual Network Embedding with Global Attention via Optimal Transport
Liqun Chen, Guoyin Wang, Chenyang Tao, Dinghan Shen, Pengyu Cheng, Xinyuan Zhang, Wenlin Wang, Yizhe Zhang, Lawrence Carin
Abstract
Constituting highly informative network embeddings is an essential 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.- Anthology ID:
- P19-1512
- Volume:
- Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2019
- Address:
- Florence, Italy
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 5193–5202
- Language:
- URL:
- https://aclanthology.org/P19-1512
- DOI:
- 10.18653/v1/P19-1512
- Cite (ACL):
- Liqun Chen, Guoyin Wang, Chenyang Tao, Dinghan Shen, Pengyu Cheng, Xinyuan Zhang, Wenlin Wang, Yizhe Zhang, and Lawrence Carin. 2019. Improving Textual Network Embedding with Global Attention via Optimal Transport. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 5193–5202, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- Improving Textual Network Embedding with Global Attention via Optimal Transport (Chen et al., ACL 2019)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/P19-1512.pdf
- Data
- Cora