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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
We propose Unsupervised Episode Generation method called Neighbors as Queries (NaQ) to solve the Few-Shot Node-Classification (FSNC) task by unsupervised Graph Meta-learning.
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
