Wasserstein Autoencoders with Mixture of Gaussian Priors for Stylized Text Generation

Author:

Ghabussi Amirpasha,Mou Lili,Vechtomova Olga

Publisher

Springer International Publishing

Reference22 articles.

1. Bahuleyan, H., Mou, L., Vechtomova, O., Poupart, P.: Variational attention for sequence-to-sequence models. In: Proceedings of the 27th International Conference on Computational Linguistics, pp. 1672–1682. Association for Computational Linguistics (2018). https://www.aclweb.org/anthology/C18-1142

2. Bahuleyan, H., Mou, L., Zhou, H., Vechtomova, O.: Stochastic Wasserstein autoencoder for probabilistic sentence generation. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 4068–4076 (2019). https://www.aclweb.org/anthology/N19-1411

3. Ben-Yosef, M., Weinshall, D.: Gaussian mixture generative adversarial networks for diverse datasets, and the unsupervised clustering of images. arXiv preprint arXiv:1808.10356 (2018). https://arxiv.org/abs/1808.10356

4. Fu, Z., Tan, X., Peng, N., Zhao, D., Yan, R.: Style transfer in text: exploration and evaluation. In: Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, pp. 663–670 (2018). https://ojs.aaai.org/index.php/AAAI/article/view/11330

5. Gao, X., et al.: Structuring latent spaces for stylized response generation. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, pp. 1814–1823 (2019). https://www.aclweb.org/anthology/D19-1190

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