Unsupervised Machine Commenting with Neural Variational Topic Model
Table 2: The performance of the unsupervised models and supervised models under the retrieval evaluation settings. (Recall@k, MRR: higher is better; MR: lower is better.)
['[EMPTY]', '[BOLD] Model', '[BOLD] Paired', '[BOLD] Unpaired', '[BOLD] Recall@1', '[BOLD] Recall@5', '[BOLD] Recall@10', '[BOLD] MR', '[BOLD] MRR']
We first compare our proposed model with other popular unsupervised methods, including TF-IDF, LDA, and NVDM. TF-IDF retrieves the comments by similarity of words rather than the semantic meaning, so it achieves low scores on all the retrieval metrics. The neural variational document model is based on the neural VAE framework. It can capture the semantic information, so it has better performance than the TF-IDF model. LDA models the topic information, and captures the deeper relationship between the article and comments, so it achieves improvement in all relevance metrics. Finally, our proposed model outperforms all these unsupervised methods, mainly because the proposed model learns both the semantics and the topic information.