Pun-GAN: Generative Adversarial Network for Pun Generation
This addresses the challenge of pun generation for natural language processing applications, but it is incremental as it adapts existing GAN methods to a specific linguistic task.
The paper tackled the problem of generating pun sentences given word sense pairs without a large pun corpus by proposing Pun-GAN, a generative adversarial network that uses reinforcement learning to train a generator, resulting in more ambiguous and diverse sentences in evaluations.
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.