96 citations · 248 across the 12 of their papers we have counts for
12 papers
Interpretable Propaganda Detection in News Articles
Seunghak Yu, Giovanni Da San Martino, Mitra Mohtarami +2
Online users today are exposed to misleading and propagandistic news articles and media posts on a daily basis. To counter thus, a number of approaches have been designed aiming to…
Cooperative Self-training of Machine Reading Comprehension
Hongyin Luo, Shang-Wen Li, Mingye Gao +2
Pretrained language models have significantly improved the performance of downstream language understanding tasks, including extractive question answering, by providing high-qualit…
AutoKG: Constructing Virtual Knowledge Graphs from Unstructured Documents for Question Answering
Seunghak Yu, Tianxing He, James Glass
Knowledge graphs (KGs) have the advantage of providing fine-grained detail for question-answering systems. Unfortunately, building a reliable KG is time-consuming and expensive as…
A Survey on Computational Propaganda Detection
Giovanni Da San Martino, Stefano Cresci, Alberto Barron-Cedeno +3
Propaganda campaigns aim at influencing people's mindset with the purpose of advancing a specific agenda. They exploit the anonymity of the Internet, the micro-profiling ability of…
Prta: A System to Support the Analysis of Propaganda Techniques in the News
Giovanni Da San Martino, Shaden Shaar, Yifan Zhang +3
Recent events, such as the 2016 US Presidential Campaign, Brexit and the COVID-19 "infodemic", have brought into the spotlight the dangers of online disinformation. There has been…
Experiments in Detecting Persuasion Techniques in the News
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 rol…
Fine-Grained Analysis of Propaganda in News Articles
Giovanni Da San Martino, Seunghak Yu, Alberto Barrón-Cedeño +2
Propaganda aims at influencing people's mindset with the purpose of advancing a specific agenda. Previous work has addressed propaganda detection at the document level, typically l…
Ensemble-Based Deep Reinforcement Learning for Chatbots
Heriberto Cuayáhuitl, Donghyeon Lee, Seonghan Ryu +7
Trainable chatbots that exhibit fluent and human-like conversations remain a big challenge in artificial intelligence. Deep Reinforcement Learning (DRL) is promising for addressing…
Factor Graph Attention
Idan Schwartz, Seunghak Yu, Tamir Hazan +1
Dialog is an effective way to exchange information, but subtle details and nuances are extremely important. While significant progress has paved a path to address visual dialog wit…
Syllable-level Neural Language Model for Agglutinative Language
Seunghak Yu, Nilesh Kulkarni, Haejun Lee +1
Language models for agglutinative languages have always been hindered in past due to myriad of agglutinations possible to any given word through various affixes. We propose a metho…
An Embedded Deep Learning based Word Prediction
Seunghak Yu, Nilesh Kulkarni, Haejun Lee +1
Recent developments in deep learning with application to language modeling have led to success in tasks of text processing, summarizing and machine translation. However, deploying…
Deep Reinforcement Learning for Multi-Domain Dialogue Systems
Heriberto Cuayáhuitl, Seunghak Yu, Ashley Williamson +1
Standard deep reinforcement learning methods such as Deep Q-Networks (DQN) for multiple tasks (domains) face scalability problems. We propose a method for multi-domain dialogue pol…