The DOI matches its Crossref record; 50 of the 50 references it cites were found in a registry or an index.
Adversarial Learning for Neural Dialogue Generation
Open accessAbstract
In this paper, drawing intuition from the Turing test, we propose using adversarial training for open-domain dialogue generation: the system is trained to produce sequences that are indistinguishable from human-generated dialogue utterances.We cast the task as a reinforcement learning (RL) problem where we jointly train two systems, a generative model to produce response sequences, and a discriminator-analagous to the human evaluator in the Turing test-to distinguish between the human-generated dialogues and the machine-generated ones.The outputs from the discriminator are then used as rewards for the generative model, pushing the system to generate dialogues that mostly resemble human dialogues.In addition to adversarial training we describe a model for adversarial evaluation that uses success in fooling an adversary as a dialogue evaluation metric, while avoiding a number of potential pitfalls.Experimental results on several metrics, including adversarial evaluation, demonstrate that the adversarially-trained system generates higher-quality responses than previous baselines.
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Cites
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GAN（Generative Adversarial Nets）
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Neural Machine Translation by Jointly Learning to Align and Translate
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Mastering the game of Go with deep neural networks and tree search
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Unsupervised representation learning with deep convolutional generative adversarial networks
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Effective Approaches to Attention-based Neural Machine Translation
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Improved Techniques for Training GANs
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Computing Machinery and Intelligence (1950)
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Cited by
Improved training of Wasserstein GANs
Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., & Courville, A. (2017) · arXiv · doi:10.48550/arxiv.1704.00028 · Cited by 6,800
Spectral Normalization for Generative Adversarial Networks
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“…adversarial networks (GANs) (Goodfellow et al., 2014) have been enjoying considerable success as a framework of generative models in recent years, and it has been applied to numerous types of tasks and datasets (Radford et al., 2016; Salimans et al., 2016; Ho & Ermon, 2016; Li et al., 2017).”
MethodRecent Trends in Deep Learning Based Natural Language Processing [Review Article]
Young, T., Hazarika, D., Poria, S., & Cambria, E. (2018) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2018.2840738 · Cited by 2,921
Adversarial Examples for Evaluating Reading Comprehension Systems
Jia, R., & Liang, P. (2017) · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d17-1215 · Cited by 1,410
The Curious Case of Neural Text Degeneration
Holtzman, A., Buys, J., Du, L., Forbes, M., & Choi, Y. (2019) · arXiv · doi:10.48550/arxiv.1904.09751 · Cited by 1,304
Metadata
- Authors
- Li, J., Monroe, W., Shi, T., Jean, S., Ritter, A., & Jurafsky, D.
- Venue
- Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
- Published
- 2017
- Pages
- 2157-2169
- OpenAlex
- W2581637843
- Cited by
- 873
- References
- 50
- FWCI
- 73.04
- Type
- conference paper
- Language
- en
- Open access
- hybrid
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- Li, J., Monroe, W., Shi, T., Jean, S., Ritter, A., & Jurafsky, D. (2017). Adversarial Learning for Neural Dialogue Generation. Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. https://doi.org/10.18653/v1/d17-1230
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