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Found 19 papers, 3 papers with code
Entity and Evidence Guided Document-Level Relation Extraction
In this paper, we propose a novel framework E2GRE (Entity and Evidence Guided Relation Extraction) that jointly extracts relations and the underlying evidence sentences by using large pretrained language model (LM) as input encoder.
EfficientSRFace: An Efficient Network with Super-Resolution Enhancement for Accurate Face Detection
In face detection, low-resolution faces, such as numerous small faces of a human group in a crowded scene, are common in dense face prediction tasks.
Imbalanced Node Classification Beyond Homophilic Assumption
minority classes to balance the label and topology distribution.
EfficientFace: An Efficient Deep Network with Feature Enhancement for Accurate Face Detection
Besides, this is conducive to estimating the locations of faces and enhancing the descriptive power of face features.
SpanDrop: Simple and Effective Counterfactual Learning for Long Sequences
Distilling supervision signal from a long sequence to make predictions is a challenging task in machine learning, especially when not all elements in the input sequence contribute equally to the desired output.
Improving Time Sensitivity for Question Answering over Temporal Knowledge Graphs
These questions often involve three time-related challenges that previous work fail to adequately address: 1) questions often do not specify exact timestamps of interest (e. g., "Obama" instead of 2000); 2) subtle lexical differences in time relations (e. g., "before" vs "after"); 3) off-the-shelf temporal KG embeddings that previous work builds on ignore the temporal order of timestamps, which is crucial for answering temporal-order related questions.
Ranked #2 on
Question Answering
on CronQuestions
Open Temporal Relation Extraction for Question Answering
Understanding the temporal relations among events in text is a critical aspect of reading comprehension, which can be evaluated in the form of temporal question answering (TQA).
Graph Ensemble Learning over Multiple Dependency Trees for Aspect-level Sentiment Classification
Recent work on aspect-level sentiment classification has demonstrated the efficacy of incorporating syntactic structures such as dependency trees with graph neural networks(GNN), but these approaches are usually vulnerable to parsing errors.
Ensemble Learning Based Classification Algorithm Recommendation
Recommending appropriate algorithms to a classification problem is one of the most challenging issues in the field of data mining.
Multi-hop Attention Graph Neural Network
Currently, at every layer, attention is computed between connected pairs of nodes and depends solely on the representation of the two nodes.
Inductive Learning on Commonsense Knowledge Graph Completion
Here, we propose to study the inductive learning setting for CKG completion where unseen entities may present at test time.
Entity and Evidence Guided Relation Extraction for DocRED
Document-level relation extraction is a challenging task which requires reasoning over multiple sentences in order to predict relations in a document.
Ranked #14 on
Relation Extraction
on DocRED
Improving Neural Language Generation with Spectrum Control
Recent Transformer-based models such as Transformer-XL and BERT have achieved huge success on various natural language processing tasks.
Orthogonal Relation Transforms with Graph Context Modeling for Knowledge Graph Embedding
Translational distance-based knowledge graph embedding has shown progressive improvements on the link prediction task, from TransE to the latest state-of-the-art RotatE.
Ranked #18 on
Link Prediction
on FB15k-237
Select, Answer and Explain: Interpretable Multi-hop Reading Comprehension over Multiple Documents
Interpretable multi-hop reading comprehension (RC) over multiple documents is a challenging problem because it demands reasoning over multiple information sources and explaining the answer prediction by providing supporting evidences.
Improving Graph Attention Networks with Large Margin-based Constraints
Graph Attention Networks (GATs) are the state-of-the-art neural architecture for representation learning with graphs.
Selective Attention Based Graph Convolutional Networks for Aspect-Level Sentiment Classification
Aspect-level sentiment classification aims to identify the sentiment polarity towards a specific aspect term in a sentence.
Multi-hop Reading Comprehension across Multiple Documents by Reasoning over Heterogeneous Graphs
We introduce a heterogeneous graph with different types of nodes and edges, which is named as Heterogeneous Document-Entity (HDE) graph.
A Feature Subset Selection Algorithm Automatic Recommendation Method
The performance of the candidate FSS algorithms is evaluated by a multi-criteria metric that takes into account not only the classification accuracy over the selected features, but also the runtime of feature selection and the number of selected features.
