no code implementations • NAACL 2018 • Ramy Baly, Mitra Mohtarami, James Glass, Lluis Marquez, Alessandro Moschitti, Preslav Nakov
A reasonable approach for fact checking a claim involves retrieving potentially relevant documents from different sources (e. g., news websites, social media, etc.
no code implementations • NAACL 2018 • Mitra Mohtarami, Ramy Baly, James Glass, Preslav Nakov, Lluis Marquez, Alessandro Moschitti
We present a novel end-to-end memory network for stance detection, which jointly (i) predicts whether a document agrees, disagrees, discusses or is unrelated with respect to a given target claim, and also (ii) extracts snippets of evidence for that prediction.
Ranked #6 on Fake News Detection on FNC-1
no code implementations • COLING 2016 • Salvatore Romeo, Giovanni Da San Martino, Alberto Barr{\'o}n-Cede{\~n}o, Aless Moschitti, ro, Yonatan Belinkov, Wei-Ning Hsu, Yu Zhang, Mitra Mohtarami, James Glass
In real-world data, e. g., from Web forums, text is often contaminated with redundant or irrelevant content, which leads to introducing noise in machine learning algorithms.
no code implementations • 6 Feb 2019 • 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.
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.
no code implementations • SEMEVAL 2019 • Tsvetomila Mihaylova, Georgi Karadjov, Pepa Atanasova, Ramy Baly, Mitra Mohtarami, Preslav Nakov
For subtask A, all systems improved over the majority class baseline.
no code implementations • NAACL 2019 • Hadi Amiri, Mitra Mohtarami
We present Vector of Locally Aggregated Embeddings (VLAE) for effective and, ultimately, lossless representation of textual content.
no code implementations • NAACL 2019 • Moin Nadeem, Wei Fang, Brian Xu, Mitra Mohtarami, James Glass
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, stance detection of documents with respect to given claims, evidence extraction, and linguistic analysis.
no code implementations • IJCNLP 2019 • Mitra Mohtarami, James Glass, Preslav Nakov
In particular, we introduce a novel contrastive language adaptation approach applied to memory networks, which ensures accurate alignment of stances in the source and target languages, and can effectively deal with the challenge of limited labeled data in the target language.
no code implementations • WS 2019 • Wei Fang, Moin Nadeem, Mitra Mohtarami, James Glass
We present a multi-task learning model that leverages large amount of textual information from existing datasets to improve stance prediction.
no code implementations • RANLP 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.
no code implementations • ACL 2021 • Hadi Amiri, Mitra Mohtarami, Isaac Kohane
We present a text representation approach that can combine different views (representations) of the same input through effective data fusion and attention strategies for ranking purposes.
3 code implementations • 8 Mar 2018 • Tsvetomila Mihaylova, Preslav Nakov, Lluis Marquez, Alberto Barron-Cedeno, Mitra Mohtarami, Georgi Karadzhov, James Glass
Community Question Answering (cQA) forums are very popular nowadays, as they represent effective means for communities around particular topics to share information.
1 code implementation • 4 Aug 2019 • Pepa Atanasova, Preslav Nakov, Lluís Màrquez, Alberto Barrón-Cedeño, Georgi Karadzhov, Tsvetomila Mihaylova, Mitra Mohtarami, James Glass
We study the problem of automatic fact-checking, paying special attention to the impact of contextual and discourse information.