Natural language processing (NLP) is a branch of artificial intelligence, which aims to understand human language. NLP combines computational linguistics rule-based modeling of human language with statistical, machine learning, or deep learning models. NLP transforms human language into machine-understandable representation, and gives machines the ability to understand human emotions and intentions. NLP plays a growing role in business solutions, such as customer relationship management (CRM) or employee productivity. Our research focuses on representation learning, deep learning, and semi-supervised learning algorithms for various tasks in NLP.

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Learning from Missing Relations: Contrastive Learning with Commonsense Knowledge Graphs for Commonsense Inference to appear in Findings of ACL: ACL 2022 (full)

KOAS: Korean Text Offensiveness Analysis System EMNLP 2021 (Demo)

Multi-pretraining for Large-scale Text Classification Findings of ACL: EMNLP 2020 (full)

Adaptive Compression of Word Embeddings ACL 2020 (full)

From Small-scale to Large-scale Text Classification WWW 2019 (full)

Learning to Generate Word Representations using Subword Information COLING 2018 (full)