Adversarial evaluation for open-domain dialogue generation

2017-08-01 · WS 2017 8 · Elia Bruni, Raquel Fern{\'a}ndez

We investigate the potential of adversarial evaluation methods for open-domain dialogue generation systems, comparing the performance of a discriminative agent to that of humans on the same task. Our results show that the task is hard, both for automated models and humans, but that a discriminative agent can learn patterns that lead to above-chance performance.

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