Unsupervised Machine Commenting with Neural Variational Topic Model
This work addresses the challenge of low data availability for training commenting systems in online communities, offering an incremental improvement by leveraging unpaired data to enhance engagement.
The authors tackled the problem of automatically generating article comments without requiring parallel data, proposing an unsupervised approach using a neural variational topic model to retrieve comments based on topic similarity, which outperformed previous lexicon-based models and achieved state-of-the-art performance in semi-supervised scenarios.
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.