Recent graph-to-text models generate text from graph-based data using either global or local aggregation to learn node representations. Global node encoding allows explicit communication between two distant nodes, thereby neglecting graph topology as all nodes are directly connected. In contrast, local node encoding considers the relations between neighbor nodes capturing the graph structure, but it can fail to capture long-range relations. In this work, we gather both encoding strategies, proposing novel neural models which encode an input graph combining both global and local node contexts, in order to learn better contextualized node embeddings. In our experiments, we demonstrate that our approaches lead to significant improvements on two graph-to-text datasets achieving BLEU scores of 18.01 on AGENDA dataset, and 63.69 on the WebNLG dataset for seen categories, outperforming state-of-the-art models by 3.7 and 3.1 points, respectively.
Modeling Graph Structure via Relative Position for Text Generation from Knowledge Graphs
Center for Information and Language Processing (CIS), LMU Munich（慕尼黑大学语言与信息处理中心）；Research Training Group AIPHES and UKP Lab, Technische Universität Darmstadt（达姆施塔特工业大学）
Hound: Hunting Supervision Signals for Few and Zero Shot Node Classification on Text-attributed Graph
School of Computer Science, Wuhan University（武汉大学计算机学院）；Centre for Perceptual and Interactive Intelligence (CPII)（感知与交互智能中心）；OceanBase, Ant Group（蚂蚁集团OceanBase）
Embedding Biomedical Ontologies by Jointly Encoding Network Structure and Textual Node Descriptors
Athens University of Economics and Business（雅典经济与商业大学）；Institute for Language and Speech Processing, Research Center ‘Athena’（“雅典娜”研究中心语言与语音处理研究所）；Google Research（谷歌研究院）；CNRS（法国国家科学研究中心）；Univ. Paris Diderot（巴黎第七大学）
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学 polled 智能学院）；DIRO, Université de Montréal（蒙特利尔大学 DIRO）；Beijing Key Laboratory of Big Data Management and Analysis Methods（北京大数据管理与分析方法重点实验室）
Topics and Label Propagation: Best of Both Worlds for Weakly Supervised Text Classification
TCS Research, Tata Consultancy Services（塔塔咨询服务公司研究部）；Indian Institute of Technology Bombay（印度理工学院孟买分校）；Indian Institute of Technology Madras（印度理工学院马德拉斯分校）
