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Found 5 papers, 4 papers with code
Interpretable Prototype-based Graph Information Bottleneck
In this work, we propose a novel framework of explainable GNNs, called interpretable Prototype-based Graph Information Bottleneck (PGIB) that incorporates prototype learning within the information bottleneck framework to provide prototypes with the key subgraph from the input graph that is important for the model prediction.
Unsupervised Episode Generation for Graph Meta-learning
Despite the effectiveness of graph contrastive learning (GCL) methods in the FSNC task without using the label information, they mainly learn generic node embeddings without consideration of the downstream task to be solved, which may limit its performance in the FSNC task.
KLUE: Korean Language Understanding Evaluation
We introduce Korean Language Understanding Evaluation (KLUE) benchmark.
An Empirical Study on Measuring the Similarity of Sentential Arguments with Language Model Domain Adaptation
Measuring the similarity between two different sentential arguments is an important task in argument mining.
Semantic Relation Classification via Bidirectional LSTM Networks with Entity-aware Attention using Latent Entity Typing
Our model not only utilizes entities and their latent types as features effectively but also is more interpretable by visualizing attention mechanisms applied to our model and results of LET.
Ranked #25 on
Relation Extraction
on SemEval-2010 Task-8
