Much research in recent years has focused on automatic article commenting. However, few of previous studies focus on the controllable generation of comments. Besides, they tend to generate dull and commonplace comments, which further limits their practical application. In this paper, we make the first step towards controllable generation of comments, by building a system that can explicitly control the emotion of the generated comments. To achieve this, we associate each kind of emotion category with an embedding and adopt a dynamic fusion mechanism to fuse this embedding into the decoder. A sentence-level emotion classifier is further employed to better guide the model to generate comments expressing the desired emotion. To increase the diversity of the generated comments, we propose a hierarchical copy mechanism that allows our model to directly copy words from the input articles. We also propose a restricted beam search (RBS) algorithm to increase intra-sentence diversity. Experimental results show that our model can generate informative and diverse comments that express the desired emotions with high accuracy.

翻译：近些年来,许多研究都集中在自动评论文章上。然而,前几份研究很少侧重于可控的生成评论。此外,它们往往产生乏味和普通的评论,从而进一步限制其实际应用。在本文中,我们为可控的生成评论迈出了第一步,建立了一个能够明确控制所生成评论的情感的系统。为了实现这一点,我们将每种情感类别与嵌入并采用动态聚合机制将这种嵌入解码器结合在一起。还进一步使用一个句级情感分类器来更好地指导模型生成表达所希望的情感的评论。为了增加所生成的评论的多样性,我们提议了一个分级复制机制,使我们的模型能够直接复制输入文章中的文字。我们还提议了一种限制的波束搜索算法,以增加内流的多样性。实验结果表明,我们的模型能够产生信息丰富和多样的评论,高精确地表达所希望的情感。