Paper:
Sequence-to-Sequence (Seq2Seq) neural models have become popular for text generation problems, e.g. neural machine translation (NMT) Britz et al., 2017), text summarization (Nallapati et al., 2017;Wang & Ling, 2016), and image captioning (Venugopalan et al., 2015;. Though sequential modeling has been shown to be effective, the dependency graph among words contains additional semantic information, and thus can be utilized for sentence modeling. In this paper, we propose a Graph-Sequence-to-Sequence (GraphSeq2Seq) model to fuse the dependency graph among words into the traditional Seq2Seq framework. For each sample, the sub-graph of each word is encoded to a graph representation, which is then utilized to sequential encoding. At last, a sequence decoder is leveraged for output generation. Since above model fuses different features by contacting them together to encode, we also propose a variant of our model that regards the graph representations as additional annotations in attention mechanism  by separately encoding different features. Experiments on several translation benchmarks show that our models can outperform existing state-of-the-art methods, demonstrating the effectiveness of the combination of Graph2Seq and Seq2Seq.

Review:
[Summary]
This paper proposes a Graph-Sequence-to-Sequence (GraphSeq2Seq) model to fuse the dependency graph among words into the traditional Seq2Seq framework.
[clarity]
This paper is basically well written though there are several grammatical errors (I guess the authors can fix them).
Motivation and goal are clear.
[originality]
Several previous methods have already tackled to integrate graph structures into seq2seq models.
Therefore, from this perspective, this study is incremental rather than innovative.
However, the core idea of the proposed method, that is, combining the word representation, sub-graph state, incoming and outgoing representations seems to be novel.
[significance]
The experimental setting used in this paper is slightly out of the current main stream of NMT research.
For example, the current top-line NMT systems uses subword unit for input and output sentences, but this paper doesn’t.
Moreover, the experiments were performed only on the very small datasets, IWSLT-2014 and 2015, which have at most 153K training parallel sentences.
Therefore, it is unclear whether the proposed method has essential effectiveness to improve the performance on the top-line NMT baselines.
Comparing on the small datasets, the proposed method seems to significantly improve the performance over current best results of NPMT+LM.
Overall, I like the idea of utilizing sub-graphs for simplicity and saving the computational cost to encode a structural (grammatical or semantic) information.
However, I really wonder if this type of technique really works well on the large training datasets...