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

By Yanbin Zhao, Lu Chen, Zhi Chen, Ruisheng Cao, Su Zhu, Kai Yu. 2020

31 citations recorded by the source corpus.

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 first-order 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 higher-order 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.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Graph Learning, Natural Language Generation

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Computer Vision and Pattern Recognition

In Graph Learning

In Natural Language Generation

Citations in the corpus

DART: Open-Domain Structured Data Record to Text Generation (2020, background): “…et al., 2016; Fer-reira et al., 2017; Song et al., 2017; Zhu et al., 2019a; Ribeiro et al., 2019; Damonte and Co-hen, 2019; Wang et al., 2020a; Zhao et al., 2020b; Shen et al., 2020), Minimal Recursion Semantics (MRS) (Hajdik et al., 2019), Resource Description Framework (RDF triples)…”
De-Confounded Variational Encoder-Decoder for Logical Table-to-Text Generation (2021, methodology): “With the different types of inputs, this task can be deﬁned more speciﬁcally, such as abstract meaning representation to text (Zhao et al., 2020; Bai et al., 2020a), infobox with key-value pairs to text (Bai et al., 2020b), graph-to-text (Song et al., 2020), and table-to-text (Wang et al., 2020;…”