Search Results for author: Jaewoo Kang

Found 46 papers, 34 papers with code

Deformable Graph Transformer

no code implementations29 Jun 2022 Jinyoung Park, Seongjun Yun, Hyeonjin Park, Jaewoo Kang, Jisu Jeong, Kyung-Min Kim, Jung-Woo Ha, Hyunwoo J. Kim

Then, the sparse attention is applied to the node sequences for learning node representations with a reduced computational cost.

Natural Language Processing

Towards More Realistic Generation of Information-Seeking Conversations

no code implementations25 May 2022 Gangwoo Kim, Sungdong Kim, Kang Min Yoo, Jaewoo Kang

In this paper, we introduce a novel framework SimSeek (simulating information-seeking conversation from unlabeled documents) and compare two variants of it to provide a deeper perspective into the information-seeking behavior.

Conversational Search

Refining Query Representations for Dense Retrieval at Test Time

no code implementations25 May 2022 Mujeen Sung, Jungsoo Park, Jaewoo Kang, Danqi Chen, Jinhyuk Lee

Dense retrieval uses a contrastive learning framework to learn dense representations of queries and contexts.

Contrastive Learning Passage Retrieval

Lack of Fluency is Hurting Your Translation Model

no code implementations24 May 2022 Jaehyo Yoo, Jaewoo Kang

While the most train sentences are created via automatic techniques such as crawling and sentence-alignment methods, the test sentences are annotated with the consideration of fluency by human.

Machine Translation Translation

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

1 code implementation6 Jan 2022 Mujeen Sung, Minbyul Jeong, Yonghwa Choi, Donghyeon Kim, Jinhyuk Lee, Jaewoo Kang

In biomedical natural language processing, named entity recognition (NER) and named entity normalization (NEN) are key tasks that enable the automatic extraction of biomedical entities (e. g., diseases and chemicals) from the ever-growing biomedical literature.

graph construction named-entity-recognition +2

Simple Questions Generate Named Entity Recognition Datasets

1 code implementation16 Dec 2021 Hyunjae Kim, Jaehyo Yoo, Seunghyun Yoon, Jinhyuk Lee, Jaewoo Kang

Recent named entity recognition (NER) models often rely on human-annotated datasets requiring the vast engagement of professional knowledge on the target domain and entities.

few-shot-ner Few-shot NER +3

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

no code implementations20 Nov 2021 Hyunjae Kim, Mujeen Sung, Wonjin Yoon, Sungjoon Park, Jaewoo Kang

This paper is a technical report on our system submitted to the chemical identification task of the BioCreative VII Track 2 challenge.

named-entity-recognition NER

Towards simple time-to-event modeling: optimizing neural networks via rank regression

no code implementations29 Sep 2021 Hyunjun Lee, Junhyun Lee, Taehwa Choi, Jaewoo Kang, Sangbum Choi

Time-to-event analysis, also known as survival analysis, aims to predict the first occurred event time, conditional on a set of features.

Representation Learning Survival Analysis +1

Can Language Models be Biomedical Knowledge Bases?

1 code implementation EMNLP 2021 Mujeen Sung, Jinhyuk Lee, Sean Yi, Minji Jeon, Sungdong Kim, Jaewoo Kang

To this end, we create the BioLAMA benchmark, which is comprised of 49K biomedical factual knowledge triples for probing biomedical LMs.

Natural Language Processing

FaVIQ: FAct Verification from Information-seeking Questions

1 code implementation ACL 2022 Jungsoo Park, Sewon Min, Jaewoo Kang, Luke Zettlemoyer, Hannaneh Hajishirzi

Claims in FAVIQ are verified to be natural, contain little lexical bias, and require a complete understanding of the evidence for verification.

Fact Checking Fact Verification +1

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

1 code implementation ACL 2021 Gangwoo Kim, Hyunjae Kim, Jungsoo Park, Jaewoo Kang

One of the main challenges in conversational question answering (CQA) is to resolve the conversational dependency, such as anaphora and ellipsis.

Question Rewriting

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

1 code implementation11 Jun 2021 Seongjun Yun, Minbyul Jeong, Sungdong Yoo, Seunghun Lee, Sean S. Yi, Raehyun Kim, Jaewoo Kang, Hyunwoo J. Kim

Despite the success of GNNs, most existing GNNs are designed to learn node representations on the fixed and homogeneous graphs.

Node Classification

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

1 code implementation CVPR 2021 Bumsoo Kim, Junhyun Lee, Jaewoo Kang, Eun-Sol Kim, Hyunwoo J. Kim

Human-Object Interaction (HOI) detection is a task of identifying "a set of interactions" in an image, which involves the i) localization of the subject (i. e., humans) and target (i. e., objects) of interaction, and ii) the classification of the interaction labels.

Human-Object Interaction Detection object-detection +1

Regularization for Long Named Entity Recognition

1 code implementation15 Apr 2021 Minbyul Jeong, Jaewoo Kang

Pre-trained language models (PLMs) are used to solve NER tasks and tend to be biased toward dataset patterns such as length statistics, surface form, and skewed class distribution.

named-entity-recognition NER

Sequence tagging for biomedical extractive question answering

1 code implementation15 Apr 2021 Wonjin Yoon, Richard Jackson, Aron Lagerberg, Jaewoo Kang

Following general domain EQA models, current biomedical EQA (BioEQA) models utilize the single-span extraction setting with post-processing steps.

Question Answering

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

no code implementations15 Jan 2021 Buru Chang, Inggeol Lee, Hyunjae Kim, Jaewoo Kang

Several machine learning-based spoiler detection models have been proposed recently to protect users from spoilers on review websites.

Machine Learning

Learning to Balance with Incremental Learning

no code implementations1 Jan 2021 Joel Jang, Yoonjeon Kim, Jaewoo Kang

Classification tasks require balanced distribution of data in order to ensure the learner to be trained to generalize over all classes.

Incremental Learning

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

1 code implementation1 Jan 2021 Hyunjae Kim, Jaewoo Kang

The number of biomedical literature on new biomedical concepts is rapidly increasing, which necessitates a reliable biomedical named entity recognition (BioNER) model for identifying new and unseen entity mentions.

named-entity-recognition NER

Learning Dense Representations of Phrases at Scale

4 code implementations ACL 2021 Jinhyuk Lee, Mujeen Sung, Jaewoo Kang, Danqi Chen

Open-domain question answering can be reformulated as a phrase retrieval problem, without the need for processing documents on-demand during inference (Seo et al., 2019).

Open-Domain Question Answering Question Generation +3

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

1 code implementation ECCV 2020 Byungjoo Kim, Bryce Chudomelka, Jinyoung Park, Jaewoo Kang, Youngjoon Hong, Hyunwoo J. Kim

Motivated by the SSP property and a generalized Runge-Kutta method, we propose Strong Stability Preserving networks (SSP networks) which improve robustness against adversarial attacks.

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

no code implementations10 Jul 2020 Jinho Lee, Raehyun Kim, Seok-Won Yi, Jaewoo Kang

Generating an investment strategy using advanced deep learning methods in stock markets has recently been a topic of interest.

Management Multi-agent Reinforcement Learning +1

Transferability of Natural Language Inference to Biomedical Question Answering

2 code implementations1 Jul 2020 Minbyul Jeong, Mujeen Sung, Gangwoo Kim, Donghyeon Kim, Wonjin Yoon, Jaehyo Yoo, Jaewoo Kang

We observe that BioBERT trained on the NLI dataset obtains better performance on Yes/No (+5. 59%), Factoid (+0. 53%), List type (+13. 58%) questions compared to performance obtained in a previous challenge (BioASQ 7B Phase B).

Natural Language Inference Question Answering +1

Biomedical Entity Representations with Synonym Marginalization

3 code implementations ACL 2020 Mujeen Sung, Hwisang Jeon, Jinhyuk Lee, Jaewoo Kang

In this way, we avoid the explicit pre-selection of negative samples from more than 400K candidates.

Look at the First Sentence: Position Bias in Question Answering

1 code implementation EMNLP 2020 Miyoung Ko, Jinhyuk Lee, Hyunjae Kim, Gangwoo Kim, Jaewoo Kang

In this study, we hypothesize that when the distribution of the answer positions is highly skewed in the training set (e. g., answers lie only in the k-th sentence of each passage), QA models predicting answers as positions can learn spurious positional cues and fail to give answers in different positions.

Question Answering

Adversarial Subword Regularization for Robust Neural Machine Translation

1 code implementation Findings of the Association for Computational Linguistics 2020 Jungsoo Park, Mujeen Sung, Jinhyuk Lee, Jaewoo Kang

Exposing diverse subword segmentations to neural machine translation (NMT) models often improves the robustness of machine translation as NMT models can experience various subword candidates.

Machine Translation Translation

Contextualized Sparse Representations for Real-Time Open-Domain Question Answering

3 code implementations ACL 2020 Jinhyuk Lee, Minjoon Seo, Hannaneh Hajishirzi, Jaewoo Kang

Open-domain question answering can be formulated as a phrase retrieval problem, in which we can expect huge scalability and speed benefit but often suffer from low accuracy due to the limitation of existing phrase representation models.

Information Retrieval Open-Domain Question Answering

Graph Transformer Networks

1 code implementation NeurIPS 2019 Seongjun Yun, Minbyul Jeong, Raehyun Kim, Jaewoo Kang, Hyunwoo J. Kim

In this paper, we propose Graph Transformer Networks (GTNs) that are capable of generating new graph structures, which involve identifying useful connections between unconnected nodes on the original graph, while learning effective node representation on the new graphs in an end-to-end fashion.

General Classification Link Prediction +2

Pre-trained Language Model for Biomedical Question Answering

3 code implementations18 Sep 2019 Wonjin Yoon, Jinhyuk Lee, Donghyeon Kim, Minbyul Jeong, Jaewoo Kang

The recent success of question answering systems is largely attributed to pre-trained language models.

Language Modelling Question Answering

HATS: A Hierarchical Graph Attention Network for Stock Movement Prediction

3 code implementations7 Aug 2019 Raehyun Kim, Chan Ho So, Minbyul Jeong, Sang-Hoon Lee, Jinkyu Kim, Jaewoo Kang

Methods that use relational data for stock market prediction have been recently proposed, but they are still in their infancy.

Graph Attention Graph Classification +2

SAIN: Self-Attentive Integration Network for Recommendation

1 code implementation27 May 2019 Seoungjun Yun, Raehyun Kim, Miyoung Ko, Jaewoo Kang

To deal with this problem, content based recommendation models which use the auxiliary attributes of users and items have been proposed.

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

1 code implementation16 May 2019 Donghyeon Park, Keonwoo Kim, Yonggyu Park, Jungwoon Shin, Jaewoo Kang

As a vast number of ingredients exist in the culinary world, there are countless food ingredient pairings, but only a small number of pairings have been adopted by chefs and studied by food researchers.

Self-Attention Graph Pooling

2 code implementations17 Apr 2019 Junhyun Lee, Inyeop Lee, Jaewoo Kang

In particular, studies have focused on generalizing convolutional neural networks to graph data, which includes redefining the convolution and the downsampling (pooling) operations for graphs.

Graph Classification

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

1 code implementation28 Feb 2019 Jinho Lee, Raehyun Kim, Yookyung Koh, Jaewoo Kang

Moreover, the results show that future stock prices can be predicted even if the training and testing procedures are done in different countries.

Stock Market Prediction

Typeface Completion with Generative Adversarial Networks

2 code implementations9 Nov 2018 Yonggyu Park, Junhyun Lee, Yookyung Koh, Inyeop Lee, Jinhyuk Lee, Jaewoo Kang

However, in designing a typeface, it is difficult to keep the style of various characters consistent, especially for languages with lots of morphological variations such as Chinese.

Image-to-Image Translation Translation +1

CollaboNet: collaboration of deep neural networks for biomedical named entity recognition

2 code implementations21 Sep 2018 Wonjin Yoon, Chan Ho So, Jinhyuk Lee, Jaewoo Kang

Our model has successfully reduced the number of misclassified entities and improved the performance by leveraging multiple datasets annotated for different entity types.

named-entity-recognition NER +1

Learning User Preferences and Understanding Calendar Contexts for Event Scheduling

1 code implementation5 Sep 2018 Donghyeon Kim, Jinhyuk Lee, Donghee Choi, Jaehoon Choi, Jaewoo Kang

With online calendar services gaining popularity worldwide, calendar data has become one of the richest context sources for understanding human behavior.

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