131 citations · 291 across the 34 of their papers we have counts for
6 papers · 1 filter
Detecting Incongruity Between News Headline and Body Text via a Deep Hierarchical Encoder
Seunghyun Yoon, Kunwoo Park, Joongbo Shin +4
Some news headlines mislead readers with overrated or false information, and identifying them in advance will better assist readers in choosing proper news stories to consume. This…
Multimodal Speech Emotion Recognition Using Audio and Text
Seunghyun Yoon, Seokhyun Byun, Kyomin Jung
Speech emotion recognition is a challenging task, and extensive reliance has been placed on models that use audio features in building well-performing classifiers. In this paper, w…
Improving Neural Question Generation using Answer Separation
Yanghoon Kim, Hwanhee Lee, Joongbo Shin +1
Neural question generation (NQG) is the task of generating a question from a given passage with deep neural networks. Previous NQG models suffer from a problem that a significant p…
Comparative Studies of Detecting Abusive Language on Twitter
Younghun Lee, Seunghyun Yoon, Kyomin Jung
The context-dependent nature of online aggression makes annotating large collections of data extremely difficult. Previously studied datasets in abusive language detection have bee…
Number Sequence Prediction Problems for Evaluating Computational Powers of Neural Networks
Hyoungwook Nam, Segwang Kim, Kyomin Jung
Inspired by number series tests to measure human intelligence, we suggest number sequence prediction tasks to assess neural network models' computational powers for solving algorit…
AttnConvnet at SemEval-2018 Task 1: Attention-based Convolutional Neural Networks for Multi-label Emotion Classification
Yanghoon Kim, Hwanhee Lee, Kyomin Jung
In this paper, we propose an attention-based classifier that predicts multiple emotions of a given sentence. Our model imitates human's two-step procedure of sentence understanding…