Search Results for author: Seunghak Yu

Found 18 papers, 4 papers with code

Interpretable Propaganda Detection in News Articles

no code implementations29 Aug 2021 Seunghak Yu, Giovanni Da San Martino, Mitra Mohtarami, James Glass, Preslav Nakov

Online users today are exposed to misleading and propagandistic news articles and media posts on a daily basis.

Propaganda detection

Cooperative Learning of Zero-Shot Machine Reading Comprehension

no code implementations12 Mar 2021 Hongyin Luo, Shang-Wen Li, Seunghak Yu, James Glass

REGEX is built upon a masked answer extraction task with an interactive learning environment containing an answer entity REcognizer, a question Generator, and an answer EXtractor.

Machine Reading Comprehension Question Answering +3

A Survey on Computational Propaganda Detection

no code implementations15 Jul 2020 Giovanni Da San Martino, Stefano Cresci, Alberto Barron-Cedeno, Seunghak Yu, Roberto Di Pietro, Preslav Nakov

Propaganda campaigns aim at influencing people's mindset with the purpose of advancing a specific agenda.

Propaganda detection

Experiments in Detecting Persuasion Techniques in the News

no code implementations15 Nov 2019 Seunghak Yu, Giovanni Da San Martino, Preslav Nakov

Many recent political events, like the 2016 US Presidential elections or the 2018 Brazilian elections have raised the attention of institutions and of the general public on the role of Internet and social media in influencing the outcome of these events.

Ensemble-Based Deep Reinforcement Learning for Chatbots

no code implementations27 Aug 2019 Heriberto Cuayáhuitl, Donghyeon Lee, Seonghan Ryu, Yongjin Cho, Sungja Choi, Satish Indurthi, Seunghak Yu, Hyungtak Choi, Inchul Hwang, Jihie Kim

Experimental results using chitchat data reveal that (1) near human-like dialogue policies can be induced, (2) generalisation to unseen data is a difficult problem, and (3) training an ensemble of chatbot agents is essential for improved performance over using a single agent.

Chatbot

Factor Graph Attention

1 code implementation CVPR 2019 Idan Schwartz, Seunghak Yu, Tamir Hazan, Alexander Schwing

We address this issue and develop a general attention mechanism for visual dialog which operates on any number of data utilities.

Graph Attention Question Answering +2

Supervised Clustering of Questions into Intents for Dialog System Applications

no code implementations EMNLP 2018 Iryna Haponchyk, Antonio Uva, Seunghak Yu, Olga Uryupina, Aless Moschitti, ro

Modern automated dialog systems require complex dialog managers able to deal with user intent triggered by high-level semantic questions.

Chatbot Intent Detection +2

On-Device Neural Language Model Based Word Prediction

1 code implementation COLING 2018 Seunghak Yu, Nilesh Kulkarni, Haejun Lee, Jihie Kim

Recent developments in deep learning with application to language modeling have led to success in tasks of text processing, summarizing and machine translation.

Language Modelling Machine Translation +4

Syllable-level Neural Language Model for Agglutinative Language

no code implementations WS 2017 Seunghak Yu, Nilesh Kulkarni, Haejun Lee, Jihie Kim

Language models for agglutinative languages have always been hindered in past due to myriad of agglutinations possible to any given word through various affixes.

Language Modelling

An Embedded Deep Learning based Word Prediction

1 code implementation6 Jul 2017 Seunghak Yu, Nilesh Kulkarni, Haejun Lee, Jihie Kim

Recent developments in deep learning with application to language modeling have led to success in tasks of text processing, summarizing and machine translation.

Language Modelling Machine Translation +1

Deep Reinforcement Learning for Multi-Domain Dialogue Systems

1 code implementation26 Nov 2016 Heriberto Cuayáhuitl, Seunghak Yu, Ashley Williamson, Jacob Carse

Standard deep reinforcement learning methods such as Deep Q-Networks (DQN) for multiple tasks (domains) face scalability problems.

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