Multi-Task Ordinal Regression for Jointly Predicting the Trustworthiness and the Leading Political Ideology of News Media

no code implementations • NAACL 2019 • Ramy Baly, Georgi Karadzhov, Abdelrhman Saleh, James Glass, Preslav Nakov

In the context of fake news, bias, and propaganda, we study two important but relatively under-explored problems: (i) trustworthiness estimation (on a 3-point scale) and (ii) political ideology detection (left/right bias on a 7-point scale) of entire news outlets, as opposed to evaluating individual articles.

Team QCRI-MIT at SemEval-2019 Task 4: Propaganda Analysis Meets Hyperpartisan News Detection

no code implementations • SEMEVAL 2019 • Abdelrhman Saleh, Ramy Baly, Alberto Barrón-Cedeño, Giovanni Da San Martino, Mitra Mohtarami, Preslav Nakov, James Glass

In this paper, we describe our submission to SemEval-2019 Task 4 on Hyperpartisan News Detection.

regression

Hierarchical Reinforcement Learning for Open-Domain Dialog

1 code implementation • 17 Sep 2019 • Abdelrhman Saleh, Natasha Jaques, Asma Ghandeharioun, Judy Hanwen Shen, Rosalind Picard

Open-domain dialog generation is a challenging problem; maximum likelihood training can lead to repetitive outputs, models have difficulty tracking long-term conversational goals, and training on standard movie or online datasets may lead to the generation of inappropriate, biased, or offensive text.

Hierarchical Reinforcement Learning Open-Domain Dialog +2

Probing Neural Dialog Models for Conversational Understanding

1 code implementation • WS 2020 • Abdelrhman Saleh, Tovly Deutsch, Stephen Casper, Yonatan Belinkov, Stuart Shieber

The predominant approach to open-domain dialog generation relies on end-to-end training of neural models on chat datasets.

Open-Domain Dialog