1 code implementation • NeurIPS 2023 • Sangwoo Seo, Sungwon Kim, Chanyoung Park
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.
1 code implementation • 27 Jun 2023 • Jihyeong Jung, Sangwoo Seo, Sungwon Kim, Chanyoung Park
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.
3 code implementations • 20 May 2021 • Sungjoon Park, Jihyung Moon, Sungdong Kim, Won Ik Cho, Jiyoon Han, Jangwon Park, Chisung Song, JunSeong Kim, Yongsook Song, Taehwan Oh, Joohong Lee, Juhyun Oh, Sungwon Lyu, Younghoon Jeong, InKwon Lee, Sangwoo Seo, Dongjun Lee, Hyunwoo Kim, Myeonghwa Lee, Seongbo Jang, Seungwon Do, Sunkyoung Kim, Kyungtae Lim, Jongwon Lee, Kyumin Park, Jamin Shin, Seonghyun Kim, Lucy Park, Alice Oh, Jung-Woo Ha, Kyunghyun Cho
We introduce Korean Language Understanding Evaluation (KLUE) benchmark.
no code implementations • 19 Feb 2021 • ChaeHun Park, Sangwoo Seo
Measuring the similarity between two different sentential arguments is an important task in argument mining.
5 code implementations • 23 Jan 2019 • Joohong Lee, Sangwoo Seo, Yong Suk Choi
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