1. D. Bahdanau, K. Cho, Y. Bengio, Neural machine translation by jointly learning to align and translate, 2014, arXiv preprint arXiv:1409.0473.
2. J. Bastings, I. Titov, W. Aziz, D. Marcheggiani, K. Sima’an, Graph convolutional encoders for syntax-aware neural machine translation, in: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics, Copenhagen, Denmark, 2017, pp. 1957–1967. https://www.aclweb.org/anthology/D17-1209, 10.18653/v1/D17-1209.
3. D. Beck, G. Haffari, T. Cohn, Graph-to-sequence learning using gated graph neural networks, in: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Association for Computational Linguistics, Melbourne, Australia, 2018, pp. 273–283. https://www.aclweb.org/anthology/P18-1026, 10.18653/v1/P18-1026.
4. Latent dirichlet allocation;Blei;Journal of Machine Learning Research,2003
5. M. Damonte, S.B. Cohen, Structural neural encoders for AMR-to-text generation, in: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), Association for Computational Linguistics, Minneapolis, Minnesota, 2019, pp. 3649–3658. https://www.aclweb.org/anthology/N19-1366, 10.18653/v1/N19-1366.