Search Results for author: Yanghoon Kim

Found 5 papers, 0 papers with code

Self-Adapter at SemEval-2021 Task 10: Entropy-based Pseudo-Labeler for Source-free Domain Adaptation

no code implementations SEMEVAL 2021 Sangwon Yoon, Yanghoon Kim, Kyomin Jung

Source-free domain adaptation is an emerging line of work in deep learning research since it is closely related to the real-world environment.

Domain Adaptation

Collaborative Training of GANs in Continuous and Discrete Spaces for Text Generation

no code implementations16 Oct 2020 Yanghoon Kim, Seungpil Won, Seunghyun Yoon, Kyomin Jung

Applying generative adversarial networks (GANs) to text-related tasks is challenging due to the discrete nature of language.

Text Generation

MILAB at SemEval-2019 Task 3: Multi-View Turn-by-Turn Model for Context-Aware Sentiment Analysis

no code implementations SEMEVAL 2019 Yoonhyung Lee, Yanghoon Kim, Kyomin Jung

This paper describes our system for SemEval-2019 Task 3: EmoContext, which aims to predict the emotion of the third utterance considering two preceding utterances in a dialogue.

Sentiment Analysis

Improving Neural Question Generation using Answer Separation

no code implementations7 Sep 2018 Yanghoon Kim, Hwanhee Lee, Joongbo Shin, Kyomin Jung

Previous NQG models suffer from a problem that a significant proportion of the generated questions include words in the question target, resulting in the generation of unintended questions.

Question Generation

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