CrossAug: A Contrastive Data Augmentation Method for Debiasing Fact Verification Models

1 code implementation • 30 Sep 2021 • Minwoo Lee, Seungpil Won, Juae Kim, Hwanhee Lee, Cheoneum Park, Kyomin Jung

Specifically, we employ a two-stage augmentation pipeline to generate new claims and evidences from existing samples.

Data Augmentation Fact Verification

A Method to Generate a Machine-Labeled Data for Biomedical Named Entity Recognition with Various Sub-Domains

no code implementations • WS 2017 • Juae Kim, Sunjae Kwon, Youngjoong Ko, Jungyun Seo

To generate a large amount of machine-labeled data, firstly we generate an initial machine-labeled data by using a chunker and MetaMap.

BIG-bench Machine Learning named-entity-recognition +3

KSAnswer: Question-answering System of Kangwon National University and Sogang University in the 2016 BioASQ Challenge

no code implementations • WS 2016 • Hyeon-gu Lee, Minkyoung Kim, Harksoo Kim, Juae Kim, Sunjae Kwon, Jungyun Seo, Yi-reun Kim, Jung-Kyu Choi

Language Modelling Morphological Analysis +1

ThisIsCompetition at SemEval-2019 Task 9: BERT is unstable for out-of-domain samples

no code implementations • SEMEVAL 2019 • Cheoneum Park, Juae Kim, Hyeon-gu Lee, Reinald Kim Amplayo, Harksoo Kim, Jungyun Seo, Chang-Ki Lee

This paper describes our system, Joint Encoders for Stable Suggestion Inference (JESSI), for the SemEval 2019 Task 9: Suggestion Mining from Online Reviews and Forums.

Sentence Suggestion mining +2

Factual Error Correction for Abstractive Summaries Using Entity Retrieval

no code implementations • 18 Apr 2022 • Hwanhee Lee, Cheoneum Park, Seunghyun Yoon, Trung Bui, Franck Dernoncourt, Juae Kim, Kyomin Jung

In this paper, we propose an efficient factual error correction system RFEC based on entities retrieval post-editing process.

Abstractive Text Summarization Entity Retrieval +1