b'Jaewoo Kang'

research

∙ 07/28/2023

Co-attention Graph Pooling for Efficient Pairwise Graph Interaction Learning

Graph Neural Networks (GNNs) have proven to be effective in processing a...

0 Junhyun Lee, et al. ∙

research

∙ 07/16/2023

Towards Flexible Time-to-event Modeling: Optimizing Neural Networks via Rank Regression

Time-to-event analysis, also known as survival analysis, aims to predict...

0 Hyunjun Lee, et al. ∙

research

∙ 07/10/2023

KU-DMIS-MSRA at RadSum23: Pre-trained Vision-Language Model for Radiology Report Summarization

In this paper, we introduce CheXOFA, a new pre-trained vision-language m...

0 Gangwoo Kim, et al. ∙

research

∙ 04/21/2023

KitchenScale: Learning to predict ingredient quantities from recipe contexts

Determining proper quantities for ingredients is an essential part of co...

0 Donghee Choi, et al. ∙

research

∙ 04/11/2023

ELVIS: Empowering Locality of Vision Language Pre-training with Intra-modal Similarity

Deep learning has shown great potential in assisting radiologists in rea...

0 Sumin Seo, et al. ∙

research

∙ 02/03/2023

LIQUID: A Framework for List Question Answering Dataset Generation

Question answering (QA) models often rely on large-scale training datase...

0 Seongyun Lee, et al. ∙

research

∙ 12/01/2022

Biomedical NER for the Enterprise with Distillated BERN2 and the Kazu Framework

In order to assist the drug discovery/development process, pharmaceutica...

0 Wonjin Yoon, et al. ∙

research

∙ 10/24/2022

Enhancing Label Consistency on Document-level Named Entity Recognition

Named entity recognition (NER) is a fundamental part of extracting infor...

0 Minbyul Jeong, et al. ∙

research

∙ 10/14/2022

Automatic Creation of Named Entity Recognition Datasets by Querying Phrase Representations

Most weakly supervised named entity recognition (NER) models rely on dom...

0 Hyunjae Kim, et al. ∙

research

∙ 10/14/2022

RecipeMind: Guiding Ingredient Choices from Food Pairing to Recipe Completion using Cascaded Set Transformer

We propose a computational approach for recipe ideation, a downstream ta...

0 Mogan Gim, et al. ∙

research

∙ 06/29/2022

Deformable Graph Transformer

Transformer-based models have been widely used and achieved state-of-the...

6 Jinyoung Park, et al. ∙

research

∙ 06/09/2022

Neo-GNNs: Neighborhood Overlap-aware Graph Neural Networks for Link Prediction

Graph Neural Networks (GNNs) have been widely applied to various fields ...

0 Seongjun Yun, et al. ∙

research

∙ 05/25/2022

Refining Query Representations for Dense Retrieval at Test Time

Dense retrieval uses a contrastive learning framework to learn dense rep...

0 Mujeen Sung, et al. ∙

research

∙ 05/25/2022

Towards More Realistic Generation of Information-Seeking Conversations

In this paper, we introduce a novel framework SimSeek (simulating inform...

0 Gangwoo Kim, et al. ∙

research

∙ 05/24/2022

Lack of Fluency is Hurting Your Translation Model

Many machine translation models are trained on bilingual corpus, which c...

0 Jaehyo Yoo, et al. ∙

research

∙ 01/06/2022

BERN2: an advanced neural biomedical named entity recognition and normalization tool

In biomedical natural language processing, named entity recognition (NER...

0 Mujeen Sung, et al. ∙

research

∙ 12/16/2021

Simple Questions Generate Named Entity Recognition Datasets

Named entity recognition (NER) is a task of extracting named entities of...

0 Hyunjae Kim, et al. ∙

research

∙ 11/20/2021

Improving Tagging Consistency and Entity Coverage for Chemical Identification in Full-text Articles

This paper is a technical report on our system submitted to the chemical...

0 Hyunjae Kim, et al. ∙

research

∙ 09/15/2021

Can Language Models be Biomedical Knowledge Bases?

Pre-trained language models (LMs) have become ubiquitous in solving vari...

0 Mujeen Sung, et al. ∙

research

∙ 07/05/2021

FaVIQ: FAct Verification from Information-seeking Questions

Despite significant interest in developing general purpose fact checking...

0 Jungsoo Park, et al. ∙

research

∙ 06/22/2021

Learn to Resolve Conversational Dependency: A Consistency Training Framework for Conversational Question Answering

One of the main challenges in conversational question answering (CQA) is...

0 Gangwoo Kim, et al. ∙

research

∙ 06/11/2021

Graph Transformer Networks: Learning Meta-path Graphs to Improve GNNs

Graph Neural Networks (GNNs) have been widely applied to various fields ...

0 Seongjun Yun, et al. ∙

research

∙ 04/28/2021

HOTR: End-to-End Human-Object Interaction Detection with Transformers

Human-Object Interaction (HOI) detection is a task of identifying "a set...

0 Bumsoo Kim, et al. ∙

research

∙ 04/15/2021

Sequence Tagging for Biomedical Extractive Question Answering

Current studies in extractive question answering (EQA) have modeled sing...

0 Wonjin Yoon, et al. ∙

research

∙ 04/15/2021

Consistency Training with Virtual Adversarial Discrete Perturbation

We propose an effective consistency training framework that enforces a t...

0 Jungsoo Park, et al. ∙

research

∙ 04/15/2021

Regularizing Models via Pointwise Mutual Information for Named Entity Recognition

In Named Entity Recognition (NER), pre-trained language models have been...

0 Minbyul Jeong, et al. ∙

research

∙ 01/15/2021

"Killing Me" Is Not a Spoiler: Spoiler Detection Model using Graph Neural Networks with Dependency Relation-Aware Attention Mechanism

Several machine learning-based spoiler detection models have been propos...

0 Buru Chang, et al. ∙

research

∙ 01/01/2021

How Do Your Biomedical Named Entity Models Generalize to Novel Entities?

The number of biomedical literature on new biomedical concepts is rapidl...

0 Hyunjae Kim, et al. ∙

research

∙ 12/23/2020

Learning Dense Representations of Phrases at Scale

Open-domain question answering can be reformulated as a phrase retrieval...

0 Jinhyuk Lee, et al. ∙

research

∙ 10/20/2020

Robust Neural Networks inspired by Strong Stability Preserving Runge-Kutta methods

Deep neural networks have achieved state-of-the-art performance in a var...

0 Byungjoo Kim, et al. ∙

research

∙ 09/26/2020

Can pandemics transform scientific novelty? Evidence from COVID-19

Scientific novelty is important during the pandemic due to its critical ...

0 Meijun Liu, et al. ∙

research

∙ 07/10/2020

MAPS: Multi-agent Reinforcement Learning-based Portfolio Management System

Generating an investment strategy using advanced deep learning methods i...

0 Jinho Lee, et al. ∙

research

∙ 07/01/2020

Transferability of Natural Language Inference to Biomedical Question Answering

Biomedical question answering (QA) is a challenging problem due to the s...

0 Minbyul Jeong, et al. ∙

research

∙ 06/29/2020

Answering Questions on COVID-19 in Real-Time

The recent outbreak of the novel coronavirus is wreaking havoc on the wo...

0 Jinhyuk Lee, et al. ∙

research

∙ 05/08/2020

Building a PubMed knowledge graph

PubMed is an essential resource for the medical domain, but useful conce...

0 Jian Xu, et al. ∙

research

∙ 05/01/2020

Biomedical Entity Representations with Synonym Marginalization

Biomedical named entities often play important roles in many biomedical ...

0 Mujeen Sung, et al. ∙

research

∙ 04/30/2020

Look at the First Sentence: Position Bias in Question Answering

Many extractive question answering models are trained to predict start a...

0 Miyoung Ko, et al. ∙

research

∙ 04/29/2020

Adversarial Subword Regularization for Robust Neural Machine Translation

Exposing diverse subword segmentations to neural machine translation (NM...

0 Jungsoo Park, et al. ∙

research

∙ 04/11/2020

Exploring The Spatial Reasoning Ability of Neural Models in Human IQ Tests

Although neural models have performed impressively well on various tasks...

6 Hyunjae Kim, et al. ∙

research

∙ 02/18/2020

Learning by Semantic Similarity Makes Abstractive Summarization Better

One of the obstacles of abstractive summarization is the presence of var...

0 Wonjin Yoon, et al. ∙

research

∙ 11/07/2019

Contextualized Sparse Representation with Rectified N-Gram Attention for Open-Domain Question Answering

A sparse representation is known to be an effective means to encode prec...

0 Jinhyuk Lee, et al. ∙

research

∙ 09/18/2019

Pre-trained Language Model for Biomedical Question Answering

The recent success of question answering systems is largely attributed t...

0 Wonjin Yoon, et al. ∙

research

∙ 08/07/2019

HATS: A Hierarchical Graph Attention Network for Stock Movement Prediction

Many researchers both in academia and industry have long been interested...

0 Raehyun Kim, et al. ∙

research

∙ 05/27/2019

SAIN: Self-Attentive Integration Network for Recommendation

With the growing importance of personalized recommendation, numerous rec...

0 Seoungjun Yun, et al. ∙

research

∙ 05/16/2019

KitcheNette: Predicting and Recommending Food Ingredient Pairings using Siamese Neural Networks

As a vast number of ingredients exist in the culinary world, there are c...

0 Donghyeon Park, et al. ∙

research

∙ 04/17/2019

Self-Attention Graph Pooling

Advanced methods of applying deep learning to structured data such as gr...

0 Junhyun Lee, et al. ∙

research

∙ 03/25/2019

Predicting Multiple Demographic Attributes with Task Specific Embedding Transformation and Attention Network

Most companies utilize demographic information to develop their strategy...

0 Raehyun Kim, et al. ∙

research

∙ 02/28/2019

Global Stock Market Prediction Based on Stock Chart Images Using Deep Q-Network

We applied Deep Q-Network with a Convolutional Neural Network function a...

0 Jinho Lee, et al. ∙

research

∙ 01/25/2019

BioBERT: a pre-trained biomedical language representation model for biomedical text mining

Biomedical text mining is becoming increasingly important as the number ...

0 Jinhyuk Lee, et al. ∙

research

∙ 01/25/2019

BioBERT: pre-trained biomedical language representation model for biomedical text mining

Biomedical text mining has become more important than ever as the number...

0 Jinhyuk Lee, et al. ∙

Jaewoo Kang

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