Adversarial Evaluation of Dialogue Models
This addresses the problem of reducing human evaluation needs for dialogue systems, though it is incremental as it explores a potential method without achieving broad applicability.
The paper tackled the challenge of evaluating dialogue models by proposing an adversarial loss to assess how human-like generated responses are, but found that practical application issues remain.
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.