Search Results for author: Cheoneum Park

Found 8 papers, 2 papers with code

Augmenting Query and Passage for Retrieval-Augmented Generation using LLMs for Open-Domain Question Answering

no code implementations20 Jun 2024 Minsang Kim, Cheoneum Park, Seungjun Baek

Retrieval-augmented generation (RAG) has received much attention for Open-domain question-answering (ODQA) tasks as a means to compensate for the parametric knowledge of large language models (LLMs).

Open-Domain Question Answering Retrieval

Fast End-to-end Coreference Resolution for Korean

no code implementations Findings of the Association for Computational Linguistics 2020 Cheoneum Park, Jamin Shin, Sungjoon Park, JoonHo Lim, Changki Lee

Recently, end-to-end neural network-based approaches have shown significant improvements over traditional pipeline-based models in English coreference resolution.

coreference-resolution Knowledge Distillation

KNU-HYUNDAI's NMT system for Scientific Paper and Patent Tasks onWAT 2019

no code implementations WS 2019 Cheoneum Park, Young-Jun Jung, Kihoon Kim, Geonyeong Kim, Jae-Won Jeon, Seongmin Lee, Jun-Seok Kim, Chang-Ki Lee

In this paper, we describe the neural machine translation (NMT) system submitted by the Kangwon National University and HYUNDAI (KNU-HYUNDAI) team to the translation tasks of the 6th workshop on Asian Translation (WAT 2019).

Data Augmentation Machine Translation +2

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

SEx BiST: A Multi-Source Trainable Parser with Deep Contextualized Lexical Representations

1 code implementation CONLL 2018 KyungTae Lim, Cheoneum Park, Changki Lee, Thierry Poibeau

We describe the SEx BiST parser (Semantically EXtended Bi-LSTM parser) developed at Lattice for the CoNLL 2018 Shared Task (Multilingual Parsing from Raw Text to Universal Dependencies).

Dependency Parsing Event Extraction

KNU CI System at SemEval-2018 Task4: Character Identification by Solving Sequence-Labeling Problem

no code implementations SEMEVAL 2018 Cheoneum Park, Heejun Song, Chang-Ki Lee

Character identification is an entity-linking task that finds words referring to the same person among the nouns mentioned in a conversation and turns them into one entity.

Coreference Resolution Decoder +2

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