Abstract
Inspired by the success of self attention mechanism and Transformer architecture in sequence transduction and image generation applications, we propose novel self attention-based architectures to improve the performance of adversarial latent code-based schemes in text generation. Adversarial latent code-based text generation has recently gained a lot of attention due to its promising results. In this paper, we take a step to fortify the architectures used in these setups, specifically AAE and ARAE. We benchmark two latent code-based methods (AAE and ARAE) designed based on adversarial setups. In our experiments, the Google sentence compression dataset is utilized to compare our method with these methods using various objective and subjective measures. The experiments demonstrate the proposed (self) attention-based models outperform the state-of-the-art in adversarial code-based text generation.
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References
Al-Rfou, R., et al.: Character-level language modeling with deeper self-attention. arXiv preprint arXiv:1808.04444 (2018)
Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016)
Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014)
Bengio, S., Vinyals, O., Jaitly, N., Shazeer, N.: Scheduled sampling for sequence prediction with recurrent neural networks. In: Advances in Neural Information Processing Systems, pp. 1171–1179 (2015)
Bowman, S.R., Angeli, G., Potts, C., Manning, C.D.: A large annotated corpus for learning natural language inference. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics (2015)
Che, T., et al.: Maximum-likelihood augmented discrete generative adversarial networks. arXiv preprint arXiv:1702.07983 (2017)
Cífka, O., Severyn, A., Alfonseca, E., Filippova, K.: Eval all, trust a few, do wrong to none: comparing sentence generation models. arXiv preprint arXiv:1804.07972 (2018)
Dehghani, M., Gouws, S., Vinyals, O., Uszkoreit, J., Kaiser, Ł.: Universal transformers. arXiv preprint arXiv:1807.03819 (2018)
Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)
Graves, A.: Generating sequences with recurrent neural networks. arXiv preprint arXiv:1308.0850 (2013)
Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., Courville, A.: Improved training of wasserstein gans. arXiv preprint arXiv:1704.00028 (2017)
Guo, H.: Generating text with deep reinforcement learning. CoRR abs/1510.09202 (2015). http://arxiv.org/abs/1510.09202
Haidar, Md.A., Rezagholizadeh, M., Omri, A.D., Rashid, A.: Latent code and text-based generative adversarial networks for soft-text generation. In: NAACL-HLT 2019 (2019)
Haidar, Md.A., Rezagholizadeh, M.: TextKD-GAN: text generation using knowledge distillation and generative adversarial networks. In: Canadian AI 2019 (2019)
Hu, Z., Yang, Z., Liang, X., Salakhutdinov, R., Xing, E.P.: Toward controlled generation of text. arXiv preprint arXiv:1703.00955 (2017)
Jang, E., Gu, S., Poole, B.: Categorical reparameterization with gumbel-softmax. arXiv preprint arXiv:1611.01144 (2016)
Lamb, A.M., Goyal, A.G.A.P., Zhang, Y., Zhang, S., Courville, A.C., Bengio, Y.: Professor forcing: a new algorithm for training recurrent networks. In: Advances In Neural Information Processing Systems, pp. 4601–4609 (2016)
Makhzani, A., Shlens, J., Jaitly, N., Goodfellow, I., Frey, B.: Adversarial autoencoders. arXiv preprint arXiv:1511.05644 (2015)
Miyato, T., Kataoka, T., Koyama, M., Yoshida, Y.: Spectral normalization for generative adversarial networks. arXiv preprint arXiv:1802.05957 (2018)
Papineni, K., Roukos, S., Ward, T., Zhu, W.J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting on Association for Computational Linguistics. ACL 2002, pp. 311–318. Association for Computational Linguistics, Stroudsburg (2002). https://doi.org/10.3115/1073083.1073135
Parikh, A.P., Täckström, O., Das, D., Uszkoreit, J.: A decomposable attention model for natural language inference. arXiv preprint arXiv:1606.01933 (2016)
Radford, A., Narasimhan, K., Salimans, T., Sutskever, I.: Improving language understanding by generative pre-training. https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervised/language_understanding_paper.pdf
Sennrich, R., Haddow, B., Birch, A.: Neural machine translation of rare words with subword units. CoRR abs/1508.07909 (2015). http://arxiv.org/abs/1508.07909
Shi, Z., Chen, X., Qiu, X., Huang, X.: Towards diverse text generation with inverse reinforcement learning. arXiv preprint arXiv:1804.11258 (2018)
Su, J., Xu, J., Qiu, X., Huang, X.: Incorporating discriminator in sentence generation: a GIBBS sampling method. arXiv preprint arXiv:1802.08970 (2018)
Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems, pp. 3104–3112 (2014)
Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, pp. 5998–6008 (2017)
Yu, A.W., et al.: Qanet: combining local convolution with global self-attention for reading comprehension. arXiv preprint arXiv:1804.09541 (2018)
Yu, L., Zhang, W., Wang, J., Yu, Y.: Seqgan: sequence generative adversarial nets with policy gradient. In: AAAI, pp. 2852–2858 (2017)
Zhang, H., Goodfellow, I., Metaxas, D., Odena, A.: Self-attention generative adversarial networks. arXiv preprint arXiv:1805.08318 (2018)
Zhang, Y., et al.: Adversarial feature matching for text generation. arXiv preprint arXiv:1706.03850 (2017)
Zhu, Y., et al.: Texygen: a benchmarking platform for text generation models. In: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, SIGIR 2018, Ann Arbor, MI, USA, 08–12 July 2018, pp. 1097–1100 (2018). https://doi.org/10.1145/3209978.3210080. http://doi.acm.org/10.1145/3209978.3210080
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Gagnon-Marchand, J., Sadeghi, H., Haidar, M.A., Rezagholizadeh, M. (2019). SALSA-TEXT: Self Attentive Latent Space Based Adversarial Text Generation. In: Meurs, MJ., Rudzicz, F. (eds) Advances in Artificial Intelligence. Canadian AI 2019. Lecture Notes in Computer Science(), vol 11489. Springer, Cham. https://doi.org/10.1007/978-3-030-18305-9_10
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DOI: https://doi.org/10.1007/978-3-030-18305-9_10
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