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Found 11 papers, 3 papers with code
Search from History and Reason for Future: Two-stage Reasoning on Temporal Knowledge Graphs
Specifically, at the clue searching stage, CluSTeR learns a beam search policy via reinforcement learning (RL) to induce multiple clues from historical facts.
Link Prediction on N-ary Relational Data Based on Relatedness Evaluation
However, they mainly focus on link prediction on binary relational data, where facts are usually represented as triples in the form of (head entity, relation, tail entity).
Temporal Knowledge Graph Reasoning Based on Evolutional Representation Learning
To capture these properties effectively and efficiently, we propose a novel Recurrent Evolution network based on Graph Convolution Network (GCN), called RE-GCN, which learns the evolutional representations of entities and relations at each timestamp by modeling the KG sequence recurrently.
NeuInfer: Knowledge Inference on N-ary Facts
It aims to infer an unknown element in a partial fact consisting of the primary triple coupled with any number of its auxiliary description(s).
Improving Neural Abstractive Document Summarization with Explicit Information Selection Modeling
Information selection is the most important component in document summarization task.
Ranked #25 on
Abstractive Text Summarization
on CNN / Daily Mail
Improving Neural Abstractive Document Summarization with Structural Regularization
Recent neural sequence-to-sequence models have shown significant progress on short text summarization.
Ranked #36 on
Abstractive Text Summarization
on CNN / Daily Mail
Document Embedding Enhanced Event Detection with Hierarchical and Supervised Attention
Document-level information is very important for event detection even at sentence level.
Path-Based Attention Neural Model for Fine-Grained Entity Typing
Fine-grained entity typing aims to assign entity mentions in the free text with types arranged in a hierarchical structure.
Efficient Parallel Translating Embedding For Knowledge Graphs
Knowledge graph embedding aims to embed entities and relations of knowledge graphs into low-dimensional vector spaces.
Ranked #1 on
Link Prediction
on WN18 (filtered)
Locally Adaptive Translation for Knowledge Graph Embedding
Knowledge graph embedding aims to represent entities and relations in a large-scale knowledge graph as elements in a continuous vector space.
