Recently, deep learning has made extraordinary achievements in text classification. However, most of present models, especially convolutional neural network (CNN), do not extract long-range associations, global representations, and hierarchical features well due to their relatively shallow and simple structures. This causes a negative effect on text classification. Moreover, we find that there are many express methods of texts. It is appropriate to design the multi-input model to improve the classification effect. But most of models of text classification only use words or characters and do not use the multi-input model. Inspired by the above points and Densenet (Huang et al. (2017)), we propose a new text classification model, which uses words, characters, and labels as input. The model, which is a deep CNN with a novel attention mechanism, can effectively leverage the input information and solve the above issues of the shallow model. We conduct experiments on six large text classification datasets. Our model achieves the state of the art results on all datasets compared to multiple baseline models.
Summary
This paper introduces a new model architecture for doing text classification. The main contribution is proposing a deeper CNN approach, using both word and character embeddings (as well as label embeddings with attention). The paper claims improved performance over baselines. 
Decision
I reject the paper for 3 main reasons:
1) Very misleading claims regarding establishing a new state of the art. The baselines used for comparison don't include any of the best existing published results. 
2) Lack of positioning within the literature. In particular, no mention nor discussion of Transformers (self-attention) networks, including BERT and XLNet approaches, which are the state of the art in text classification. 
3) Lack of justification/explanation for the proposed architecture. One key argument made is that current models are shallow, but it appears that only CNN models are considered for that comparison. More discussion is needed to understand why the new aspects of the proposed network are importantly different from other existing approaches.
Additional details for decision
The results from this paper are significantly inferior to the best results published. With a few quick searches, I found that there are several approaches performing better than the proposed model on every dataset considered in the analysis, as you can see below.
http://nlpprogress.com/english/text_classification.html
https://github.com/sebastianruder/NLP-progress/blob/master/english/sentiment_analysis.md
https://paperswithcode.com/sota/text-classification-on-yahoo-answers
Extra notes (not factoring in decision)
- Consider spacing out the 3 rightmost blocks in Figure 1, I found the layout confusing and there's space available.
- In section 3, I would have liked more explanation for the motivation of the various design choices.