Oana Cocarascu


2024

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ChartCheck: Explainable Fact-Checking over Real-World Chart Images
Mubashara Akhtar | Nikesh Subedi | Vivek Gupta | Sahar Tahmasebi | Oana Cocarascu | Elena Simperl
Findings of the Association for Computational Linguistics ACL 2024

Whilst fact verification has attracted substantial interest in the natural language processing community, verifying misinforming statements against data visualizations such as charts has so far been overlooked. Charts are commonly used in the real-world to summarize and com municate key information, but they can also be easily misused to spread misinformation and promote certain agendas. In this paper, we introduce ChartCheck, a novel, large-scale dataset for explainable fact-checking against real-world charts, consisting of 1.7k charts and 10.5k human-written claims and explanations. We systematically evaluate ChartCheck using vision-language and chart-to-table models, and propose a baseline to the community. Finally, we study chart reasoning types and visual attributes that pose a challenge to these models.

2023

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Reading and Reasoning over Chart Images for Evidence-based Automated Fact-Checking
Mubashara Akhtar | Oana Cocarascu | Elena Simperl
Findings of the Association for Computational Linguistics: EACL 2023

Evidence data for automated fact-checking (AFC) can be in multiple modalities such as text, tables, images, audio, or video. While there is increasing interest in using images for AFC, previous works mostly focus on detecting manipulated or fake images. We propose a novel task, chart-based fact-checking, and introduce ChartBERT as the first model for AFC against chart evidence. ChartBERT leverages textual, structural and visual information of charts to determine the veracity of textual claims. For evaluation, we create ChartFC, a new dataset of 15,886 charts. We systematically evaluate 75 different vision-language (VL) baselines and show that ChartBERT outperforms VL models, achieving 63.8% accuracy. Our results suggest that the task is complex yet feasible, with many challenges ahead.

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Multimodal Automated Fact-Checking: A Survey
Mubashara Akhtar | Michael Schlichtkrull | Zhijiang Guo | Oana Cocarascu | Elena Simperl | Andreas Vlachos
Findings of the Association for Computational Linguistics: EMNLP 2023

Misinformation is often conveyed in multiple modalities, e.g. a miscaptioned image. Multimodal misinformation is perceived as more credible by humans, and spreads faster than its text-only counterparts. While an increasing body of research investigates automated fact-checking (AFC), previous surveys mostly focus on text. In this survey, we conceptualise a framework for AFC including subtasks unique to multimodal misinformation. Furthermore, we discuss related terms used in different communities and map them to our framework. We focus on four modalities prevalent in real-world fact-checking: text, image, audio, and video. We survey benchmarks and models, and discuss limitations and promising directions for future research

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Exploring the Numerical Reasoning Capabilities of Language Models: A Comprehensive Analysis on Tabular Data
Mubashara Akhtar | Abhilash Shankarampeta | Vivek Gupta | Arpit Patil | Oana Cocarascu | Elena Simperl
Findings of the Association for Computational Linguistics: EMNLP 2023

Numerical data plays a crucial role in various real-world domains like finance, economics, and science. Thus, understanding and reasoning with numbers are essential in these fields. Recent benchmarks have assessed the numerical reasoning abilities of language models, revealing their limitations in limited and specific numerical aspects. In this paper, we propose a complete hierarchical taxonomy for numerical reasoning skills, encompassing over ten reasoning types across four levels: representation, number sense, manipulation, and complex reasoning. We conduct a comprehensive evaluation of state-of-the-art models on all reasoning types. To identify challenging reasoning types for different model types, we develop a diverse and extensive set of numerical probes and measure performance shifts. By employing a semi-automated approach, we focus on the tabular Natural Language Inference (TNLI) task as a case study. While no single model excels in all reasoning types, FlanT5 (few-/zero-shot) and GPT3.5 (few-shot) demonstrate strong overall numerical reasoning skills compared to other models in our probes.

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Proceedings of the Sixth Fact Extraction and VERification Workshop (FEVER)
Mubashara Akhtar | Rami Aly | Christos Christodoulopoulos | Oana Cocarascu | Zhijiang Guo | Arpit Mittal | Michael Schlichtkrull | James Thorne | Andreas Vlachos
Proceedings of the Sixth Fact Extraction and VERification Workshop (FEVER)

2022

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A Robustness Evaluation Framework for Argument Mining
Mehmet Sofi | Matteo Fortier | Oana Cocarascu
Proceedings of the 9th Workshop on Argument Mining

Standard practice for evaluating the performance of machine learning models for argument mining is to report different metrics such as accuracy or F1. However, little is usually known about the model’s stability and consistency when deployed in real-world settings. In this paper, we propose a robustness evaluation framework to guide the design of rigorous argument mining models. As part of the framework, we introduce several novel robustness tests tailored specifically to argument mining tasks. Additionally, we integrate existing robustness tests designed for other natural language processing tasks and re-purpose them for argument mining. Finally, we illustrate the utility of our framework on two widely used argument mining corpora, UKP topic-sentences and IBM Debater Evidence Sentence. We argue that our framework should be used in conjunction with standard performance evaluation techniques as a measure of model stability.

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PubHealthTab: A Public Health Table-based Dataset for Evidence-based Fact Checking
Mubashara Akhtar | Oana Cocarascu | Elena Simperl
Findings of the Association for Computational Linguistics: NAACL 2022

Inspired by human fact checkers, who use different types of evidence (e.g. tables, images, audio) in addition to text, several datasets with tabular evidence data have been released in recent years. Whilst the datasets encourage research on table fact-checking, they rely on information from restricted data sources, such as Wikipedia for creating claims and extracting evidence data, making the fact-checking process different from the real-world process used by fact checkers. In this paper, we introduce PubHealthTab, a table fact-checking dataset based on real world public health claims and noisy evidence tables from sources similar to those used by real fact checkers. We outline our approach for collecting evidence data from various websites and present an in-depth analysis of our dataset. Finally, we evaluate state-of-the-art table representation and pre-trained models fine-tuned on our dataset, achieving an overall F1 score of 0.73.

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Proceedings of the Fifth Fact Extraction and VERification Workshop (FEVER)
Rami Aly | Christos Christodoulopoulos | Oana Cocarascu | Zhijiang Guo | Arpit Mittal | Michael Schlichtkrull | James Thorne | Andreas Vlachos
Proceedings of the Fifth Fact Extraction and VERification Workshop (FEVER)

2021

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Proceedings of the Fourth Workshop on Fact Extraction and VERification (FEVER)
Rami Aly | Christos Christodoulopoulos | Oana Cocarascu | Zhijiang Guo | Arpit Mittal | Michael Schlichtkrull | James Thorne | Andreas Vlachos
Proceedings of the Fourth Workshop on Fact Extraction and VERification (FEVER)

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The Fact Extraction and VERification Over Unstructured and Structured information (FEVEROUS) Shared Task
Rami Aly | Zhijiang Guo | Michael Sejr Schlichtkrull | James Thorne | Andreas Vlachos | Christos Christodoulopoulos | Oana Cocarascu | Arpit Mittal
Proceedings of the Fourth Workshop on Fact Extraction and VERification (FEVER)

The Fact Extraction and VERification Over Unstructured and Structured information (FEVEROUS) shared task, asks participating systems to determine whether human-authored claims are Supported or Refuted based on evidence retrieved from Wikipedia (or NotEnoughInfo if the claim cannot be verified). Compared to the FEVER 2018 shared task, the main challenge is the addition of structured data (tables and lists) as a source of evidence. The claims in the FEVEROUS dataset can be verified using only structured evidence, only unstructured evidence, or a mixture of both. Submissions are evaluated using the FEVEROUS score that combines label accuracy and evidence retrieval. Unlike FEVER 2018, FEVEROUS requires partial evidence to be returned for NotEnoughInfo claims, and the claims are longer and thus more complex. The shared task received 13 entries, six of which were able to beat the baseline system. The winning team was “Bust a move!”, achieving a FEVEROUS score of 27% (+9% compared to the baseline). In this paper we describe the shared task, present the full results and highlight commonalities and innovations among the participating systems.

2020

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Proceedings of the Third Workshop on Fact Extraction and VERification (FEVER)
Christos Christodoulopoulos | James Thorne | Andreas Vlachos | Oana Cocarascu | Arpit Mittal
Proceedings of the Third Workshop on Fact Extraction and VERification (FEVER)

2019

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Proceedings of the Second Workshop on Fact Extraction and VERification (FEVER)
James Thorne | Andreas Vlachos | Oana Cocarascu | Christos Christodoulopoulos | Arpit Mittal
Proceedings of the Second Workshop on Fact Extraction and VERification (FEVER)

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The FEVER2.0 Shared Task
James Thorne | Andreas Vlachos | Oana Cocarascu | Christos Christodoulopoulos | Arpit Mittal
Proceedings of the Second Workshop on Fact Extraction and VERification (FEVER)

We present the results of the second Fact Extraction and VERification (FEVER2.0) Shared Task. The task challenged participants to both build systems to verify factoid claims using evidence retrieved from Wikipedia and to generate adversarial attacks against other participant’s systems. The shared task had three phases: building, breaking and fixing. There were 8 systems in the builder’s round, three of which were new qualifying submissions for this shared task, and 5 adversaries generated instances designed to induce classification errors and one builder submitted a fixed system which had higher FEVER score and resilience than their first submission. All but one newly submitted systems attained FEVER scores higher than the best performing system from the first shared task and under adversarial evaluation, all systems exhibited losses in FEVER score. There was a great variety in adversarial attack types as well as the techniques used to generate the attacks, In this paper, we present the results of the shared task and a summary of the systems, highlighting commonalities and innovations among participating systems.

2018

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Proceedings of the First Workshop on Fact Extraction and VERification (FEVER)
James Thorne | Andreas Vlachos | Oana Cocarascu | Christos Christodoulopoulos | Arpit Mittal
Proceedings of the First Workshop on Fact Extraction and VERification (FEVER)

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The Fact Extraction and VERification (FEVER) Shared Task
James Thorne | Andreas Vlachos | Oana Cocarascu | Christos Christodoulopoulos | Arpit Mittal
Proceedings of the First Workshop on Fact Extraction and VERification (FEVER)

We present the results of the first Fact Extraction and VERification (FEVER) Shared Task. The task challenged participants to classify whether human-written factoid claims could be SUPPORTED or REFUTED using evidence retrieved from Wikipedia. We received entries from 23 competing teams, 19 of which scored higher than the previously published baseline. The best performing system achieved a FEVER score of 64.21%. In this paper, we present the results of the shared task and a summary of the systems, highlighting commonalities and innovations among participating systems.

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Combining Deep Learning and Argumentative Reasoning for the Analysis of Social Media Textual Content Using Small Data Sets
Oana Cocarascu | Francesca Toni
Computational Linguistics, Volume 44, Issue 4 - December 2018

The use of social media has become a regular habit for many and has changed the way people interact with each other. In this article, we focus on analyzing whether news headlines support tweets and whether reviews are deceptive by analyzing the interaction or the influence that these texts have on the others, thus exploiting contextual information. Concretely, we define a deep learning method for relation–based argument mining to extract argumentative relations of attack and support. We then use this method for determining whether news articles support tweets, a useful task in fact-checking settings, where determining agreement toward a statement is a useful step toward determining its truthfulness. Furthermore, we use our method for extracting bipolar argumentation frameworks from reviews to help detect whether they are deceptive. We show experimentally that our method performs well in both settings. In particular, in the case of deception detection, our method contributes a novel argumentative feature that, when used in combination with other features in standard supervised classifiers, outperforms the latter even on small data sets.

2017

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Identifying attack and support argumentative relations using deep learning
Oana Cocarascu | Francesca Toni
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

We propose a deep learning architecture to capture argumentative relations of attack and support from one piece of text to another, of the kind that naturally occur in a debate. The architecture uses two (unidirectional or bidirectional) Long Short-Term Memory networks and (trained or non-trained) word embeddings, and allows to considerably improve upon existing techniques that use syntactic features and supervised classifiers for the same form of (relation-based) argument mining.