1. Paper title

Line Graph Enhanced AMR-to-Text Generation with Mix-Order Graph Attention Networks

2. link

https://www.aclweb.org/anthology/2020.acl-main.67.pdf

3. 摘要

Efficient structure encoding for graphs with labeled edges is an important yet challenging point in many graph-based models. This work focuses on AMR-to-text generation – A graph-to-sequence task aiming to recover natural language from Abstract Meaning Representations (AMR). Existing graph-to-sequence approaches generally utilize graph neural networks as their encoders, which have two limitations: 1) The message propagation process in AMR graphs is only guided by the firstorder adjacency information. 2) The relationships between labeled edges are not fully considered. In this work, we propose a novel graph encoding framework which can effectively explore the edge relations. We also adopt graph attention networks with higherorder neighborhood information to encode the rich structure in AMR graphs. Experiment results show that our approach obtains new state-of-the-art performance on English AMR benchmark datasets. The ablation analyses also demonstrate that both edge relations and higher-order information are beneficial to graph-to-sequence modeling.

4. 要解决什么问题

graph-to-sequence常用的模型具有以下局限性：
（1）AMR图中的消息传播过程仅由一阶邻接信息指导。[?]没看懂
（2）没有充分考虑标记边缘之间的关系。

5. 作者的主要贡献

新的graph encoding框架：
（1）探索边缘关系
（2）采用具有高阶邻域信息的图注意力网络对AMR图中的丰富结构进行编码

6. 得到了什么结果

English AMR benchmark datasets中性能优于SOTA。
边缘关系和高阶信息都整合到模型中。

7. 关键字

graph-to-sequence