  We introduce a novel approach for sequence decoding, Discriminative
Adversarial Search (DAS), which has the desirable properties of alleviating the
effects of exposure bias without requiring external metrics. Inspired by
Generative Adversarial Networks (GANs), wherein a discriminator is used to
improve the generator, our method differs from GANs in that the generator
parameters are not updated at training time and the discriminator is only used
to drive sequence generation at inference time.
  We investigate the effectiveness of the proposed approach on the task of
Abstractive Summarization: the results obtained show that a naive application
of DAS improves over the state-of-the-art methods, with further gains obtained
via discriminator retraining. Moreover, we show how DAS can be effective for
cross-domain adaptation. Finally, all results reported are obtained without
additional rule-based filtering strategies, commonly used by the best
performing systems available: this indicates that DAS can effectively be
deployed without relying on post-hoc modifications of the generated outputs.
