  In this paper, we focus on the task of generating a pun sentence given a pair
of word senses. A major challenge for pun generation is the lack of large-scale
pun corpus to guide the supervised learning. To remedy this, we propose an
adversarial generative network for pun generation (Pun-GAN), which does not
require any pun corpus. It consists of a generator to produce pun sentences,
and a discriminator to distinguish between the generated pun sentences and the
real sentences with specific word senses. The output of the discriminator is
then used as a reward to train the generator via reinforcement learning,
encouraging it to produce pun sentences that can support two word senses
simultaneously. Experiments show that the proposed Pun-GAN can generate
sentences that are more ambiguous and diverse in both automatic and human
evaluation.
