DynGraph2Seq: Dynamic-Graph-to-Sequence Interpretable Learning for Health Stage Prediction in Online Health Forums

S. Zhang, E. Bantum, J. Owen, and N. Elhadad, “Does sustained participation in an online health community affect sentiment?” in AMIA Annual Symposium Proceedings , vol. 2014, 2014, p. 1970

1970

Earlier work this paper cites.

S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural computation , vol. 9, no. 8, pp. 1735–1780, 1997

1997

Earlier work this paper cites.

K. Papineni, S. Roukos, T. Ward, and W.-J. Zhu, “Bleu: a method for automatic evaluation of machine translation,” in ACL 2002 , 2002, pp. 311–318

2002

Earlier work this paper cites.

C.-Y. Lin, “Rouge: A package for automatic evaluation of summaries,” Text Summarization Branches Out , 2004

2004

Earlier work this paper cites.

M. Jha and N. Elhadad, “Cancer stage prediction based on patient online discourse,” in Proceedings of the 2010 Workshop on Biomedical Natural Language Processing , 2010, pp. 64–71

2010

Earlier work this paper cites.

N. Elhadad, S. Zhang, P. Driscoll, and S. Brody, “Characterizing the sublanguage of online breast cancer forums for medications, symptoms, and emotions,” in AMIA Annual Symposium Proceedings , vol. 2014. American Medical Informatics Association, 2014, p. 516

2014

Earlier work this paper cites.

D. Bahdanau, K. Cho, and Y. Bengio, “Neural machine translation by jointly learning to align and translate,” arXiv preprint arXiv:1409.0473 , 2014

Original

2014

Earlier work this paper cites.

K. Cho, B. Van Merriënboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, and Y. Bengio, “Learning phrase representations using rnn encoder-decoder for statistical machine translation,” arXiv preprint arXiv:1406.1078 , 2014

Original

2014

Earlier work this paper cites.

I. Sutskever, O. Vinyals, and Q. V. Le, “Sequence to sequence learning with neural networks,” in NIPS 2014 , 2014, pp. 3104–3112

2014

Earlier work this paper cites.

D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980 , 2014

Original

2014

Earlier work this paper cites.

M.-T. Luong, H. Pham, and C. D. Manning, “Effective approaches to attention-based neural machine translation,” arXiv preprint arXiv:1508.04025 , 2015

Original

2015

Earlier work this paper cites.

K. S. Tai, R. Socher, and C. D. Manning, “Improved semantic representations from tree-structured long short-term memory networks,” arXiv preprint arXiv:1503.00075 , 2015

Original

2015

Cited alongside, same era.

C. Raffel and D. P. Ellis, “Feed-forward networks with attention can solve some long-term memory problems,” arXiv preprint arXiv:1512.08756 , 2015

Original

2015

Cited alongside, same era.

Z. Yang, D. Yang, C. Dyer, X. He, A. Smola, and E. Hovy, “Hierarchical attention networks for document classification,” in NAACL , 2016, pp. 1480–1489

2016

Cited alongside, same era.

A. Eriguchi, K. Hashimoto, and Y. Tsuruoka, “Tree-to-sequence attentional neural machine translation,” arXiv preprint arXiv:1603.06075 , 2016

Original

2016

Cited alongside, same era.

P. Goyal, S. R. Chhetri, and A. Canedo, “dyngraph2vec: Capturing network dynamics using dynamic graph representation learning,” arXiv preprint arXiv:1809.02657 , 2018

Original

2018

Later among the works it cites.

J. Wu, S. Pan, X. Zhu, C. Zhang, and S. Y. Philip, “Multiple structure-view learning for graph classification,” IEEE Transactions on Neural Networks and Learning Systems , vol. 29, no. 7, pp. 3236–3251, 2018

2018

Later among the works it cites.

K. Xu, L. Wu, Z. Wang, Y. Feng, and V. Sheinin, “Sql-to-text generation with graph-to-sequence model,” arXiv preprint arXiv:1809.05255 , 2018

Original

2018

Later among the works it cites.

K. Xu, L. Wu, Z. Wang, M. Yu, L. Chen, and V. Sheinin, “Exploiting rich syntactic information for semantic parsing with graph-to-sequence model,” arXiv preprint arXiv:1808.07624 , 2018

Original

2018

Later among the works it cites.