EungGyun Kim


2021

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OutFlip: Generating Examples for Unknown Intent Detection with Natural Language Attack
DongHyun Choi | Myeong Cheol Shin | EungGyun Kim | Dong Ryeol Shin
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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Deep Context- and Relation-Aware Learning for Aspect-based Sentiment Analysis
Shinhyeok Oh | Dongyub Lee | Taesun Whang | IlNam Park | Seo Gaeun | EungGyun Kim | Harksoo Kim
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

Existing works for aspect-based sentiment analysis (ABSA) have adopted a unified approach, which allows the interactive relations among subtasks. However, we observe that these methods tend to predict polarities based on the literal meaning of aspect and opinion terms and mainly consider relations implicitly among subtasks at the word level. In addition, identifying multiple aspect–opinion pairs with their polarities is much more challenging. Therefore, a comprehensive understanding of contextual information w.r.t. the aspect and opinion are further required in ABSA. In this paper, we propose Deep Contextualized Relation-Aware Network (DCRAN), which allows interactive relations among subtasks with deep contextual information based on two modules (i.e., Aspect and Opinion Propagation and Explicit Self-Supervised Strategies). Especially, we design novel self-supervised strategies for ABSA, which have strengths in dealing with multiple aspects. Experimental results show that DCRAN significantly outperforms previous state-of-the-art methods by large margins on three widely used benchmarks.

2020

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Reference and Document Aware Semantic Evaluation Methods for Korean Language Summarization
Dongyub Lee | Myeong Cheol Shin | Taesun Whang | Seungwoo Cho | Byeongil Ko | Daniel Lee | EungGyun Kim | Jaechoon Jo
Proceedings of the 28th International Conference on Computational Linguistics

Text summarization refers to the process that generates a shorter form of text from the source document preserving salient information. Many existing works for text summarization are generally evaluated by using recall-oriented understudy for gisting evaluation (ROUGE) scores. However, as ROUGE scores are computed based on n-gram overlap, they do not reflect semantic meaning correspondences between generated and reference summaries. Because Korean is an agglutinative language that combines various morphemes into a word that express several meanings, ROUGE is not suitable for Korean summarization. In this paper, we propose evaluation metrics that reflect semantic meanings of a reference summary and the original document, Reference and Document Aware Semantic Score (RDASS). We then propose a method for improving the correlation of the metrics with human judgment. Evaluation results show that the correlation with human judgment is significantly higher for our evaluation metrics than for ROUGE scores.