# Adversarial Evaluation of Dialogue Models

arXiv: https://arxiv.org/abs/1701.08198 · HTML: https://mlsanity.com/paper/1701.08198/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jan 2017.

## Built on by (most cited first, 12 of 12)

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- [Recent Advances in Neural Text Generation: A Task-Agnostic Survey](https://mlsanity.com/paper/2203.03047/) (2022)
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- [Modeling Text-visual Mutual Dependency for Multi-modal Dialog Generation](https://mlsanity.com/paper/2105.14445/) (2021)
- [The Interplay of Task Success and Dialogue Quality: An in-depth Evaluation in Task-Oriented Visual Dialogues](https://mlsanity.com/paper/2103.11151/) (2021)
- [How to Evaluate Your Dialogue Models: A Review of Approaches](https://mlsanity.com/paper/2108.01369/) (2021)
- [On the Use of Linguistic Features for the Evaluation of Generative Dialogue Systems](https://mlsanity.com/paper/2104.06335/) (2021)
- [Relevance in Dialogue: Is Less More? An Empirical Comparison of Existing Metrics, and a Novel Simple Metric](https://mlsanity.com/paper/2206.01823/) (2022)

## Abstract (arXiv)

The recent application of RNN encoder-decoder models has resulted in substantial progress in fully data-driven dialogue systems, but evaluation remains a challenge. An adversarial loss could be a way to directly evaluate the extent to which generated dialogue responses sound like they came from a human. This could reduce the need for human evaluation, while more directly evaluating on a generative task. In this work, we investigate this idea by training an RNN to discriminate a dialogue model's samples from human-generated samples. Although we find some evidence this setup could be viable, we also note that many issues remain in its practical application. We discuss both aspects and conclude that future work is warranted.