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