Posterior-GAN: Towards Informative and Coherent Response Generation with Posterior Generative Adversarial Network
arXiv:2003.02020
Abstract
Neural conversational models learn to generate responses by taking into account the dialog history. These models are typically optimized over the query-response pairs with a maximum likelihood estimation objective. However, the query-response tuples are naturally loosely coupled, and there exist multiple responses that can respond to a given query, which leads the conversational model learning burdensome. Besides, the general dull response problem is even worsened when the model is confronted with meaningless response training instances. Intuitively, a high-quality response not only responds to the given query but also links up to the future conversations, in this paper, we leverage the query-response-future turn triples to induce the generated responses that consider both the given context and the future conversations. To facilitate the modeling of these triples, we further propose a novel encoder-decoder based generative adversarial learning framework, Posterior Generative Adversarial Network (Posterior-GAN), which consists of a forward and a backward generative discriminator to cooperatively encourage the generated response to be informative and coherent by two complementary assessment perspectives. Experimental results demonstrate that our method effectively boosts the informativeness and coherence of the generated response on both automatic and human evaluation, which verifies the advantages of considering two assessment perspectives.
arXiv commentAccepted by AAAI 2020
References in corpus (10)
- A Neural Conversational Model
- How NOT To Evaluate Your Dialogue System: An Empirical Study of Unsupervised Evaluation Metrics for Dialogue Response Generation
- Deep Reinforcement Learning for Dialogue Generation
- Topic Aware Neural Response Generation
- Adversarial Learning for Neural Dialogue Generation
- Learning Discourse-level Diversity for Neural Dialog Models using Conditional Variational Autoencoders
- Sequence to Backward and Forward Sequences: A Content-Introducing Approach to Generative Short-Text Conversation
- Improving Variational Encoder-Decoders in Dialogue Generation
- Latent Variable Dialogue Models and their Diversity
- A Study of Reinforcement Learning for Neural Machine Translation