# Graph2Seq: Graph to Sequence Learning with Attention-based Neural Networks

@article{Xu2018Graph2SeqGT, title={Graph2Seq: Graph to Sequence Learning with Attention-based Neural Networks}, author={Kun Xu and Lingfei Wu and Zhiguo Wang and Yansong Feng and Vadim Sheinin}, journal={ArXiv}, year={2018}, volume={abs/1804.00823} }

The celebrated Sequence to Sequence learning (Seq2Seq) technique and its numerous variants achieve excellent performance on many tasks. However, many machine learning tasks have inputs naturally represented as graphs; existing Seq2Seq models face a significant challenge in achieving accurate conversion from graph form to the appropriate sequence. To address this challenge, we introduce a novel general end-to-end graph-to-sequence neural encoder-decoder model that maps an input graph to a…

## 116 Citations

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