  Article comments can provide supplementary opinions and facts for readers,
thereby increase the attraction and engagement of articles. Therefore,
automatically commenting is helpful in improving the activeness of the
community, such as online forums and news websites. Previous work shows that
training an automatic commenting system requires large parallel corpora.
Although part of articles are naturally paired with the comments on some
websites, most articles and comments are unpaired on the Internet. To fully
exploit the unpaired data, we completely remove the need for parallel data and
propose a novel unsupervised approach to train an automatic article commenting
model, relying on nothing but unpaired articles and comments. Our model is
based on a retrieval-based commenting framework, which uses news to retrieve
comments based on the similarity of their topics. The topic representation is
obtained from a neural variational topic model, which is trained in an
unsupervised manner. We evaluate our model on a news comment dataset.
Experiments show that our proposed topic-based approach significantly
outperforms previous lexicon-based models. The model also profits from paired
corpora and achieves state-of-the-art performance under semi-supervised
scenarios.
