Paper:
Recent years have seen remarkable progress of text generation in different contexts, including the most common setting of generating text from scratch, the increasingly popular paradigm of retrieval and editing, and others. Text infilling, which fills missing text portions of a sentence or paragraph, is also of numerous use in real life. Previous work has focused on restricted settings, by either assuming single word per missing portion, or limiting to single missing portion to the end of text. This paper studies the general task of text infilling, where the input text can have an arbitrary number of portions to be filled, each of which may require an arbitrary unknown number of tokens. We develop a self-attention model with segment-aware position encoding for precise global context modeling. We further create a variety of supervised data by masking out text in different domains with varying missing ratios and mask strategies. Extensive experiments show the proposed model performs significantly better than other methods, and generates meaningful text patches.

Review:
Summary
This paper proposes an approach to fill in gaps in sentences. While usually generation assumes a provided left context, this paper generalizes generation to any missing gap of any number of words.
The method is based on Transformer which does attention on all the available context and generates in a left to right manner.
The authors validate their approach on rather small scale datasets. I am unclear about several details of their empirical validation.
Relevance: this work is on text generation, which is very relevant to this conference.
Clarity: the description of the method is very clear, less so the description of the empirical validation.
Novelty: The method per se is not novel, but the combination of method and application is.
Empirical validation: The empirical validation is not very clear and potentially weak.
First, I did not understand the baselines the authors considered: seq2seq and GAN. There are LOTS of variants of these methods and the references cited by the authors do not immediately apply to filling-in tasks. The authors should explain in detail what these baselines are and how they work. 
Second, MaskGAN ICLR 2018 was proposed to solve the filling task and code is publicly available by the authors of that paper. A direct comparison to MaskGAN seems a must.
Third, there are lots of details that are unclear, such as : how are segments defined in practice? the ranking metric is not clearly defined (pair-wise ranking or ranking of N possible completion?). Could the authors be a little bit more formal?
Speaking of metrics, why don't the authors considered precision at K for the evaluation of small gaps?
Fourth, the datasets considered, in particular the Grimm's dataset, is very small. There are only 16K training sentences and the vocabulary size is only 7K. How big is the model? How comes it does not overfit to such a small dataset? Did the authors measure overlap between training and test sets? 
More general comments
The beauty of the proposed approach is its simplicity. However, the proposed model feels not satisfactory as generation proceeds left to right, while the rightmost and the leftmost missing word in the gap should be treated as equal citizens.
Minor note: positional embeddings are useful also with convolutional models.