70 citations · 73 across the 3 of their papers we have counts for
3 papers
cs.CL2021
Triples-to-Text Generation with Reinforcement Learning Based Graph-augmented Neural Networks
Hanning Gao, Lingfei Wu, Hongyun Zhang +4
Considering a collection of RDF triples, the RDF-to-text generation task aims to generate a text description. Most previous methods solve this task using a sequence-to-sequence mod…
cs.CL2021★ 3 cited
Graph-augmented Learning to Rank for Querying Large-scale Knowledge Graph
Hanning Gao, Lingfei Wu, Po Hu +3
Knowledge graph question answering (KGQA) based on information retrieval aims to answer a question by retrieving answer from a large-scale knowledge graph. Most existing methods fi…
cs.CL2021★ 70 cited
Graph Neural Networks for Natural Language Processing: A Survey
Lingfei Wu, Yu Chen, Kai Shen +5
Deep learning has become the dominant approach in coping with various tasks in Natural LanguageProcessing (NLP). Although text inputs are typically represented as a sequence of tok…