Papers
Divisive Language and Propaganda Detection using Multi-head Attention Transformers with Deep Learning BERT-based Language Models for Binary Classification
Norman Mapes, Anna White, Radhika Medury et al.
Fine-Tuned Neural Models for Propaganda Detection at the Sentence and Fragment levels
Tariq Alhindi, Jonas Pfeiffer, Smaranda Muresan
Neural Architectures for Fine-Grained Propaganda Detection in News
Pankaj Gupta, Khushbu Saxena, Usama Yaseen et al.
Fine-Grained Propaganda Detection with Fine-Tuned BERT
Shehel Yoosuf, Yin Yang
Unraveling the Search Space of Abusive Language in Wikipedia with Dynamic Lexicon Acquisition
Wei-Fan Chen, Khalid Al Khatib, Matthias Hagen et al.
Rumor Detection on Social Media: Datasets, Methods and Opportunities
Quanzhi Li, Qiong Zhang, Luo Si et al.
Generating Sentential Arguments from Diverse Perspectives on Controversial Topic
ChaeHun Park, Wonsuk Yang, Jong Park
Mapping (Dis-)Information Flow about the MH17 Plane Crash
Mareike Hartmann, Yevgeniy Golovchenko, Isabelle Augenstein
Calls to Action on Social Media: Detection, Social Impact, and Censorship Potential
Anna Rogers, Olga Kovaleva, Anna Rumshisky
Identifying Nuances in Fake News vs. Satire: Using Semantic and Linguistic Cues
Or Levi, Pedram Hosseini, Mona Diab et al.
How Many Users Are Enough? Exploring Semi-Supervision and Stylometric Features to Uncover a Russian Troll Farm
Nayeema Nasrin, Kim-Kwang Raymond Choo, Myung Ko et al.
Detecting context abusiveness using hierarchical deep learning
Ju-Hyoung Lee, Jun-U Park, Jeong-Won Cha et al.
Assessing Post Deletion in Sina Weibo: Multi-modal Classification of Hot Topics
Meisam Navaki Arefi, Rajkumar Pandi, Michael Carl Tschantz et al.
Proceedings of the Second Workshop on Natural Language Processing for Internet Freedom: Censorship, Disinformation, and Propaganda
Anna Feldman, Giovanni Da San Martino, Alberto Barrón-Cedeño et al.
Efficiency through Auto-Sizing: Notre Dame NLP’s Submission to the WNGT 2019 Efficiency Task
Kenton Murray, Brian DuSell, David Chiang
Selecting, Planning, and Rewriting: A Modular Approach for Data-to-Document Generation and Translation
Lesly Miculicich, Marc Marone, Hany Hassan
From Research to Production and Back: Ludicrously Fast Neural Machine Translation
Young Jin Kim, Marcin Junczys-Dowmunt, Hany Hassan et al.
Naver Labs Europe’s Systems for the Document-Level Generation and Translation Task at WNGT 2019
Fahimeh Saleh, Alexandre Berard, Ioan Calapodescu et al.
University of Edinburgh’s submission to the Document-level Generation and Translation Shared Task
Ratish Puduppully, Jonathan Mallinson, Mirella Lapata
SYSTRAN @ WNGT 2019: DGT Task
Li Gong, Josep Crego, Jean Senellart
Monash University’s Submissions to the WNGT 2019 Document Translation Task
Sameen Maruf, Gholamreza Haffari
Transformer and seq2seq model for Paraphrase Generation
Elozino Egonmwan, Yllias Chali
Learning to Generate Word- and Phrase-Embeddings for Efficient Phrase-Based Neural Machine Translation
Chan Young Park, Yulia Tsvetkov
Auto-Sizing the Transformer Network: Improving Speed, Efficiency, and Performance for Low-Resource Machine Translation
Kenton Murray, Jeffery Kinnison, Toan Q. Nguyen et al.