Search Results for author: Arnav Arora

Found 12 papers, 8 papers with code

Multi-Hop Fact Checking of Political Claims

1 code implementation10 Sep 2020 Wojciech Ostrowski, Arnav Arora, Pepa Atanasova, Isabelle Augenstein

We: 1) construct a small annotated dataset, PolitiHop, of evidence sentences for claim verification; 2) compare it to existing multi-hop datasets; and 3) study how to transfer knowledge from more extensive in- and out-of-domain resources to PolitiHop.

Claim Verification Fact Checking +1

A Survey on Stance Detection for Mis- and Disinformation Identification

no code implementations Findings (NAACL) 2022 Momchil Hardalov, Arnav Arora, Preslav Nakov, Isabelle Augenstein

Understanding attitudes expressed in texts, also known as stance detection, plays an important role in systems for detecting false information online, be it misinformation (unintentionally false) or disinformation (intentionally false information).

Fact Checking Misinformation +3

Detecting Harmful Content On Online Platforms: What Platforms Need Vs. Where Research Efforts Go

no code implementations27 Feb 2021 Arnav Arora, Preslav Nakov, Momchil Hardalov, Sheikh Muhammad Sarwar, Vibha Nayak, Yoan Dinkov, Dimitrina Zlatkova, Kyle Dent, Ameya Bhatawdekar, Guillaume Bouchard, Isabelle Augenstein

The proliferation of harmful content on online platforms is a major societal problem, which comes in many different forms including hate speech, offensive language, bullying and harassment, misinformation, spam, violence, graphic content, sexual abuse, self harm, and many other.

Abusive Language Misinformation

Cross-Domain Label-Adaptive Stance Detection

1 code implementation EMNLP 2021 Momchil Hardalov, Arnav Arora, Preslav Nakov, Isabelle Augenstein

In this paper, we perform an in-depth analysis of 16 stance detection datasets, and we explore the possibility for cross-domain learning from them.

Domain Adaptation Stance Detection

Few-Shot Cross-Lingual Stance Detection with Sentiment-Based Pre-Training

1 code implementation13 Sep 2021 Momchil Hardalov, Arnav Arora, Preslav Nakov, Isabelle Augenstein

Most research in stance detection, however, has been limited to working with a single language and on a few limited targets, with little work on cross-lingual stance detection.

Stance Detection

Probing Pre-Trained Language Models for Cross-Cultural Differences in Values

1 code implementation25 Mar 2022 Arnav Arora, Lucie-Aimée Kaffee, Isabelle Augenstein

In this paper, we introduce probes to study which values across cultures are embedded in these models, and whether they align with existing theories and cross-cultural value surveys.

Thorny Roses: Investigating the Dual Use Dilemma in Natural Language Processing

1 code implementation17 Apr 2023 Lucie-Aimée Kaffee, Arnav Arora, Zeerak Talat, Isabelle Augenstein

Dual use, the intentional, harmful reuse of technology and scientific artefacts, is a problem yet to be well-defined within the context of Natural Language Processing (NLP).

Ethics

Topic-Guided Sampling For Data-Efficient Multi-Domain Stance Detection

2 code implementations1 Jun 2023 Erik Arakelyan, Arnav Arora, Isabelle Augenstein

The results show that our method outperforms the state-of-the-art with an average of $3. 5$ F1 points increase in-domain, and is more generalizable with an averaged increase of $10. 2$ F1 on out-of-domain evaluation while using $\leq10\%$ of the training data.

Contrastive Learning Domain Adaptation +1

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