AFPun-GAN: Ambiguity-Fluency Generative Adversarial Network for Pun Generation

Abstract

Automatic pun generation is an interesting and challenging text generation task. In this study, we focus on the task of homographic pun generation by given a pair of word senses. Current efforts depend on templates or laboriously annotated pun source to guide the supervised learning, which is lack of quality and diversity of generated puns. To address this, we present a new text generation model, called Ambiguity-Fluency Pun Generative Adversarial Network (AFPun-GAN) for pun genration. This model is composed of a pun generator to produce pun sentences by a hierarchical on-lstm attention model, and a pun discriminator to distinguish the generated pun sentences and real sentences with word senses of target pun word. The proposed model assigns a hierarchical low reward to train the pun generator via reinforcement learning, encouraging the pun generator to produce the ambiguous and fluent pun sentences that can better support two word senses. The experimental results on pun generation task demonstrate that our proposed AFPun-GAN model is able to generate pun sentences that are more ambiguous and fluent in both automatic and human evaluation.