Generating Relevant Article Comments via Variational Multi-Layer Fusion

Hanyi Zou, Huifang Xu, Qingchao Kong, Yilin Cao, Wenji Mao

Abstract: Article comment generation is a novel and challenging task in natural language generation, which has attracted widespread attention from researchers in recent years. High-quality article comments such as relevant, diverse, and informative ones can greatly promote user interactions and enhance the user experience. However, current research works generally overlook the relevance between comments and the source article, which may generate mediocre and dull comments. To address this problem, a variational multi-layer fusion model (VMFM) based on variational auto-encoder (VAE) is proposed in this paper. The posterior distribution of the proposed VMFM is employed to supervise the prior network in selecting context-related latent variables from the source article, which are further integrated into the decoder to increase the relevance between generated comments and the source article. Due to the sequential nature of text generation, the influence of those latent variables on the decoder gradually diminishes during auto-regressive decoding. To mitigate this issue, we propose a multi-layer fusion method, which fuses a series of context-related latent variables extracted from the source article into every decoder layer. Experiments on four datasets show that our model significantly outperforms strong baselines in relevance, diversity, informativeness and fluency of generated comments based on automatic and human evaluations.