@inproceedings{chen-etal-2019-improving-textual,
title = "Improving Textual Network Embedding with Global Attention via Optimal Transport",
author = "Chen, Liqun and
Wang, Guoyin and
Tao, Chenyang and
Shen, Dinghan and
Cheng, Pengyu and
Zhang, Xinyuan and
Wang, Wenlin and
Zhang, Yizhe and
Carin, Lawrence",
editor = "Korhonen, Anna and
Traum, David and
M{\`a}rquez, Llu{\'\i}s",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P19-1512",
doi = "10.18653/v1/P19-1512",
pages = "5193--5202",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Improving Textual Network Embedding with Global Attention via Optimal Transport
%A Chen, Liqun
%A Wang, Guoyin
%A Tao, Chenyang
%A Shen, Dinghan
%A Cheng, Pengyu
%A Zhang, Xinyuan
%A Wang, Wenlin
%A Zhang, Yizhe
%A Carin, Lawrence
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F chen-etal-2019-improving-textual
%X 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.
%R 10.18653/v1/P19-1512
%U https://aclanthology.org/P19-1512
%U https://doi.org/10.18653/v1/P19-1512
%P 5193-5202
Markdown (Informal)
[Improving Textual Network Embedding with Global Attention via Optimal Transport](https://aclanthology.org/P19-1512) (Chen et al., ACL 2019)
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.