论文标题
通过主题感知指针生成网络生成相关和多元化的评论
Generating Pertinent and Diversified Comments with Topic-aware Pointer-Generator Networks
论文作者
论文摘要
评论生成是自然语言生成（NLG）的一项新的挑战性任务，近年来引起了很多关注。但是，先前工作产生的评论往往缺乏相关性和多样性。在本文中，我们提出了一个基于主题感知指针生成网络（TPGN）的新颖生成模型，该模型可以利用隐藏在文章中的主题信息来指导有关相关和多元化评论的产生。首先，我们设计了一个关键字级别和主题级编码器注意机制，以捕获文章中的主题信息。接下来，我们将主题信息集成到指针生成网络中，以指导评论生成。大规模评论生成数据集的实验表明，我们的模型产生了宝贵的评论，并且胜过竞争性的基线模型。
Comment generation, a new and challenging task in Natural Language Generation (NLG), attracts a lot of attention in recent years. However, comments generated by previous work tend to lack pertinence and diversity. In this paper, we propose a novel generation model based on Topic-aware Pointer-Generator Networks (TPGN), which can utilize the topic information hidden in the articles to guide the generation of pertinent and diversified comments. Firstly, we design a keyword-level and topic-level encoder attention mechanism to capture topic information in the articles. Next, we integrate the topic information into pointer-generator networks to guide comment generation. Experiments on a large scale of comment generation dataset show that our model produces the valuable comments and outperforms competitive baseline models significantly.
