The character-based model, such as BERT, has achieved remarkable success in Chinese named entity recognition (NER). However, such model would likely miss the overall information of the entity words. In this paper, we propose to combine priori entity information with BERT. Instead of relying on additional lexicons or pre-trained word embeddings, our model has generated entity classification embeddings directly on the pre-trained BERT, having the merit of increasing model practicability and avoiding OOV problem. Experiments show that our model has achieved state-of-the-art results on 3 Chinese NER datasets.
Summary:
      This paper discussed an approach to do named entity resolution (NER, the paper focuses only on Chinese NER but I think it could generalize to other languages as well). The idea is based on smart integration and extension of multiple existing building blocks: 1) BERT pre-trained model 2) a previous work to get document embedding by doing weighted average of word embedding (https://openreview.net/pdf?id=SyK00v5xx) and 3) Scaled dot-product attention mechanism applied directly to multi-label classification. The "Introduction", "Related work", and "Experiment Settings" sections are well written and covers many details and decent references. Especially, the "experiments" section is described in a great amount of details, which should be very helpful for reproducibility. 
      
Contributions:
      * The author found an interesting application of the original algorithm (https://openreview.net/pdf?id=SyK00v5xx) to represent the entity class embedding based on averaging "BERT" embeddings of all the component words. This could be implemented as a pre-processing step against any training dataset to derive "pre-learned" entity class embedding.
      * Instead of the common approach of connecting the BERT sequence outputs directly to CRF layer, the author added an intermediate layer to calculate the classification attention between a sentence (sequence of token embedding) and any entity class (based on the above pre-learned entity embedding). This result plus the original sentence embedding are concatenated.  The concatenation is further fed into a few additional layers to produce the final inputs into CRF layer.
Weakness:
     * The paper lacks novelty. As pointed above, I did not see that the contribution from the paper is sufficiently original. It is a good application of various existing methods though.
I also have a few suggestions/questions below:
* The ERNIE paper (https://arxiv.org/abs/1907.12412v1) is mentioned in the related work. Since ERNIE can potentially learn a good vocab for Chinese, did you ever compare your approach vs ERNIE+CRF? 
* There is one paper that I know which is pretty relevant to what you are doing here, which is probably worth a reference. https://arxiv.org/abs/1805.04174.  In that paper, the idea is to co-learn a class embedding and perform text classification. Their class attention is performed through dot-production attention though.
* The Table index seems wrong in your paper. (I think Table 2 is not mentioned in your paper, but all tables (3-6) is offset by 1). 
* There are some minor typos or places that need some clarifications.
   - in the abstract: "character-based" model. This is a little confusing. Because BERT is a word-piece based model. word-piece could across multiple characters for English. IIUC, You probably want to say "Chinese-character" instead of character.
   - in "Introduction", "providing greater weight to characters identical to each entity class", you might want to revise this sentence to clarify its meaning further.
   - In section 3.2, you might want to give some explanation to some notations (the first time you refer to it). For example, what is $L$, what is $m$ and $n$.  What is $S$?  Also why the denominator of Emb(Word) is not $n-m+1$? 
   - The last paragraph in section 3.3 needs more clarification as well. How do you merge the three tensors after attention stage? (a concatenation ?) . The last sentence mentioned "residential", I guess instead you want to say "residual".  You might also want to clarify where the "3 layers" of residual appear in your network.
  - In your experiment, (if I did not miss), did you freeze the BERT parameters and entity embeddings when finetuning your NER model?
  - in Table 2 and Table 3, why the 13-layer BERT + CRF performs significantly worse on Recall (Table 2) and significantly better on Recall (Table 3)?