Named entity recognition (NER) and relation extraction (RE) are two important tasks in information extraction and retrieval (IE & IR). Recent work has demonstrated that it is beneficial to learn these tasks jointly, which avoids the propagation of error inherent in pipeline-based systems and improves performance. However, state-of-the-art joint models typically rely on external natural language processing (NLP) tools, such as dependency parsers, limiting their usefulness to domains (e.g. news) where those tools perform well. The few neural, end-to-end models that have been proposed are trained almost completely from scratch. In this paper, we propose a neural, end-to-end model for jointly extracting entities and their relations which does not rely on external NLP tools and which integrates a large, pre-trained language model. Because the bulk of our model's parameters are pre-trained and we eschew recurrence for self-attention, our model is fast to train. On 5 datasets across 3 domains, our model matches or exceeds state-of-the-art performance, sometimes by a large margin.
The paper proposes a new joint learning algorithm that works for two tasks, NER and RE. The model is based on a pre-trained BERT model, which provides the word vectors of the input word sequence. Then it solves two tasks with two network branches: the first branch minimizes the loss for NER, and the second branch minimizes the loss for RE. The second branch uses entity labels predicted by the first branch, so joint learning may benefit both tasks. 
The design of the architecture is novel, but it is also not groundbreaking. Each network branch is from known structures, but the combination is not proposed before. 
The submission has evaluated the proposed algorithms on four datasets and improved SOTA performances. The ablation study justifies the design details. 
The writing is generally clear. 
Now critics: 
Ablation study: 
1. As pointed by one public comment, the ablation study should show how much improvement is from BERT vectors. 
2. I'd like to see another ablation study of whether RE helps NER. If you remove the RE component, does the NER performance suffer? 
Writing: 
3. how are predicted labels embedded? Do you learn a vector of each tag of BIOES and then take a weighted sum of these vectors with predicted probabilities as weights?