most citedAdversarial Domain Adaptation for Stance Detection

22 citations · 28 across the 4 of their papers we have counts for

collaborators

6 papers

cs.CL20191 cited

Contrastive Language Adaptation for Cross-Lingual Stance Detection

Mitra Mohtarami, James Glass, Preslav Nakov

We study cross-lingual stance detection, which aims to leverage labeled data in one language to identify the relative perspective (or stance) of a given document with respect to a…

cs.CL2019

Automatic Fact-Checking Using Context and Discourse Information

Pepa Atanasova, Preslav Nakov, Lluís Màrquez +5

We study the problem of automatic fact-checking, paying special attention to the impact of contextual and discourse information. We address two related tasks: (i) detecting check-w…

cs.CL20194 cited

FAKTA: An Automatic End-to-End Fact Checking System

Moin Nadeem, Wei Fang, Brian Xu +2

We present FAKTA which is a unified framework that integrates various components of a fact checking process: document retrieval from media sources with various types of reliability…

cs.CL20191 cited

SemEval-2019 Task 8: Fact Checking in Community Question Answering Forums

Tsvetomila Mihaylova, Georgi Karadjov, Pepa Atanasova +3

We present SemEval-2019 Task 8 on Fact Checking in Community Question Answering Forums, which features two subtasks. Subtask A is about deciding whether a question asks for factual…

cs.IR2019

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

Abdelrhman Saleh, Ramy Baly, Alberto Barrón-Cedeño +4

In this paper, we describe our submission to SemEval-2019 Task 4 on Hyperpartisan News Detection. Our system relies on a variety of engineered features originally used to detect pr…

cs.LG201922 cited

Adversarial Domain Adaptation for Stance Detection

Brian Xu, Mitra Mohtarami, James Glass

This paper studies the problem of stance detection which aims to predict the perspective (or stance) of a given document with respect to a given claim. Stance detection is a major…