2017 AdversarialLearningforNeuralDia

• (Li et al., 2017) ⇒ Jiwei Li, Will Monroe, Tianlin Shi, Sebastien Jean, Alan Ritter, and Dan Jurafsky. (2017). “Adversarial Learning for Neural Dialogue Generation.” In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (EMNLP 2017). DOI:10.18653/v1/D17-1230.

Subject Headings: Natural Language Generation Task.

Notes

• Version(s) and Link(s):
  • ACL Anthology: https://www.aclweb.org/anthology/D17-1230/
  • ArXiv: https://arxiv.org/abs/1701.06547
  • DBLP: https://dblp.org/rec/html/conf/emnlp/LiMSJRJ17

Cited By

• Google Scholar: ~ 477 Citations.
• Semantic Scholar: ~ 469 Citations.
• MS Academic: ~ 451 Citations.

Quotes

Abstract

We apply adversarial training to open-domain dialogue generation, training a system to produce sequences that are indistinguishable from human-generated dialogue utterances. We cast the task as a reinforcement learning problem where we jointly train two systems: a generative model to produce response sequences, and a discriminator - analogous to the human evaluator in the Turing test -” to distinguish between the human-generated dialogues and the machine-generated ones. In this generative adversarial network approach, the outputs from the discriminator are used to encourage the system towards more human-like dialogue. Further, we investigate models 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

BibTeX

@inproceedings{2017_AdversarialLearningforNeuralDia,
  author    = {Jiwei Li and
               Will Monroe and
               Tianlin Shi and
               Sebastien Jean and
               Alan Ritter and
               Dan Jurafsky},
  editor    = {Martha Palmer and
               Rebecca Hwa and
               Sebastian Riedel},
  title     = {Adversarial Learning for Neural Dialogue Generation},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural
               Language Processing (EMNLP 2017)},
  address   = {Copenhagen, Denmark},
  pages     = {2157--2169},
  publisher = {Association for Computational Linguistics},
  year      = {2017},
  month     = {September},
  url       = {https://doi.org/10.18653/v1/d17-1230},
  doi       = {10.18653/v1/d17-1230},
}

---

	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2017 AdversarialLearningforNeuralDia	Daniel Jurafsky Alan Ritter Jiwei Li Will Monroe Tianlin Shi Sebastien Jean			Adversarial Learning for Neural Dialogue Generation						2017