Existing models on open-domain comment generation are difficult to train, and
they produce repetitive and uninteresting responses. The problem is due to
multiple and contradictory responses from a single article, and by the rigidity
of retrieval methods. To solve this problem, we propose a combined approach to
retrieval and generation methods. We propose an attentive scorer to retrieve
informative and relevant comments by leveraging user-generated data. Then, we
use such comments, together with the article, as input for a
sequence-to-sequence model with copy mechanism. We show the robustness of our
model and how it can alleviate the aforementioned issue by using a large scale
comment generation dataset. The result shows that the proposed generative model
significantly outperforms strong baseline such as Seq2Seq with attention and
Information Retrieval models by around 27 and 30 BLEU-1 points respectively.

提出了一种结合检索和生成方法的方法，使用关注模块检索有信息量和相关性的用户生成数据评论，然后与文章一起作为输入，使用具有复制机制的序列到序列模型。在大规模评论生成数据集上展示了该模型的稳健性并证明了其在 BLEU-1 得分方面比具有注意力机制的 Seq2Seq 和信息检索模型等强基线模型显着优越约 27 和 30 个 BLEU-1 点。