Harksoo Kim


2019

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Relation Extraction among Multiple Entities Using a Dual Pointer Network with a Multi-Head Attention Mechanism
Seong Sik Park | Harksoo Kim
Proceedings of the Second Workshop on Fact Extraction and VERification (FEVER)

Many previous studies on relation extrac-tion have been focused on finding only one relation between two entities in a single sentence. However, we can easily find the fact that multiple entities exist in a single sentence and the entities form multiple relations. To resolve this prob-lem, we propose a relation extraction model based on a dual pointer network with a multi-head attention mechanism. The proposed model finds n-to-1 subject-object relations by using a forward de-coder called an object decoder. Then, it finds 1-to-n subject-object relations by using a backward decoder called a sub-ject decoder. In the experiments with the ACE-05 dataset and the NYT dataset, the proposed model achieved the state-of-the-art performances (F1-score of 80.5% in the ACE-05 dataset, F1-score of 78.3% in the NYT dataset)

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ThisIsCompetition at SemEval-2019 Task 9: BERT is unstable for out-of-domain samples
Cheoneum Park | Juae Kim | Hyeon-gu Lee | Reinald Kim Amplayo | Harksoo Kim | Jungyun Seo | Changki Lee
Proceedings of the 13th International Workshop on Semantic Evaluation

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. JESSI is a combination of two sentence encoders: (a) one using multiple pre-trained word embeddings learned from log-bilinear regression (GloVe) and translation (CoVe) models, and (b) one on top of word encodings from a pre-trained deep bidirectional transformer (BERT). We include a domain adversarial training module when training for out-of-domain samples. Our experiments show that while BERT performs exceptionally well for in-domain samples, several runs of the model show that it is unstable for out-of-domain samples. The problem is mitigated tremendously by (1) combining BERT with a non-BERT encoder, and (2) using an RNN-based classifier on top of BERT. Our final models obtained second place with 77.78% F-Score on Subtask A (i.e. in-domain) and achieved an F-Score of 79.59% on Subtask B (i.e. out-of-domain), even without using any additional external data.

2018

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Two-Step Training and Mixed Encoding-Decoding for Implementing a Generative Chatbot with a Small Dialogue Corpus
Jintae Kim | Hyeon-Gu Lee | Harksoo Kim | Yeonsoo Lee | Young-Gil Kim
Proceedings of the Workshop on Intelligent Interactive Systems and Language Generation (2IS&NLG)

2016

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KSAnswer: Question-answering System of Kangwon National University and Sogang University in the 2016 BioASQ Challenge
Hyeon-gu Lee | Minkyoung Kim | Harksoo Kim | Juae Kim | Sunjae Kwon | Jungyun Seo | Yi-reun Kim | Jung-Kyu Choi
Proceedings of the Fourth BioASQ workshop

2008

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Speakers’ Intention Prediction Using Statistics of Multi-level Features in a Schedule Management Domain
Donghyun Kim | Hyunjung Lee | Choong-Nyoung Seon | Harksoo Kim | Jungyun Seo
Proceedings of ACL-08: HLT, Short Papers

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Information extraction using finite state automata and syllable n-grams in a mobile environment
Choong-Nyoung Seon | Harksoo Kim | Jungyun Seo
Proceedings of the ACL-08: HLT Workshop on Mobile Language Processing

2002

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A Reliable Indexing Method for a Practical QA System
Harksoo Kim | Jungyun Seo
COLING-02: Multilingual Summarization and Question Answering

2001

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MAYA: A Fast Question-answering System Based on a Predictive Answer Indexer
Harksoo Kim | Kyungsun Kim | Gary Geunbae Lee | Jungyun Seo
Proceedings of the ACL 2001 Workshop on Open-Domain Question Answering

1999

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Anaphora Resolution using Extended Centen’ng Algorithm in a Multi-modal Dialogue System
Harksoo Kim | Jeong-Mi Cho | Jungyun Seo
The Relation of Discourse/Dialogue Structure and Reference