Paper:
Recently, the pre-trained language model, BERT (and its robustly optimized version RoBERTa), has attracted a lot of attention in natural language understanding (NLU), and achieved state-of-the-art accuracy in various NLU tasks, such as sentiment classification, natural language inference, semantic textual similarity and question answering. Inspired by the linearization exploration work of Elman (Elman, 1990), we extend BERT to a new model, StructBERT, by incorporating language structures into pre-training. Specifically, we pre-train StructBERT with two auxiliary tasks to make the most of the sequential order of words and sentences, which leverage language structures at the word and sentence levels, respectively. As a result, the new model is adapted to different levels of language understanding required by downstream tasks. The StructBERT with structural pre-training gives surprisingly good empirical results on a variety of downstream tasks, including pushing the state-of-the-art on the GLUE benchmark to 89.0 (outperforming all published models at the time of model submission), the F1 score on SQuAD v1.1 question answering to 93.0, the accuracy on SNLI to 91.7.

Review:
This paper proposes to use additional structures within and between sentences for pre-training BERT. The basic idea is to shuffle either some n-grams within sentences or the sentences in texts, then train the model to predict the correct orders. Experiments in this work show that, with this additional training objective, the proposed pre-trained model, StructBERT, obtains good performance on the tasks including natural language understanding and question answering.
Overall, I think the experiments and results in this work are not sufficient enough to support the claim:
- It is necessary to show the performance of BERT only trained with the proposed word and sentence objectives. Otherwise, it is not clear how much benefit the model can get from them and the work is basically incremental. 
- Some justification is needed about why choosing trigrams and why 5% is a good number of sampling trigrams from texts
Besides, there are some recent work on analyzing why BERT encodes any linguistic properties of texts, for example 
- Goldberg. Assessing BERT's syntactic abilities. 2019
- Tenny et al. BERT Rediscovers the Classical NLP Pipeline. ACL 2019
- Tenny et al. What do you learn from context? ICLR 2019
All of them show positive results on BERT can capture some syntactic information from text automatically. Which makes me wonder why the simple additional training objective proposed in this work can still lead to performance improvement. Is there an explanation?