Papers by Arkaitz Zubiaga
All-in-one: Multi-task Learning for Rumour Verification (C18-1)
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| Challenge: | Automatic resolution of rumours is a challenging task that can be broken down into smaller components that make up a pipeline . previous work focused on rumor detection, rumou tracking and stance classification as separate components . |
| Approach: | They propose a multi-task learning approach that allows joint training of main and auxiliary tasks, improving the performance of rumour verification. |
| Outcome: | The proposed approach improves the performance of rumour verification by combining main and auxiliary tasks into one pipeline. |
MAPLE: Micro Analysis of Pairwise Language Evolution for Few-Shot Claim Verification (2024.findings-eacl)
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| Challenge: | Existing methods for verification of claims are limited by the availability of labeled data. |
| Approach: | They propose a method that explores the alignment between a claim and its evidence using a seq2seq model and a novel semantic measure. |
| Outcome: | The proposed method shows significant performance improvements over baselines SEED, PET and LLaMA 2 across three fact-checking datasets. |
Zero-shot and Few-shot Learning with Instruction-following LLMs for Claim Matching in Automated Fact-checking (2025.coling-main)
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| Challenge: | Claim matching (CM) is a binary classification task that can be used to determine if two claims can be verified using the same piece of evidence or fact-check. |
| Approach: | They propose a claim matching task that uses binary classification and large language models to test out learning approaches to the task. |
| Outcome: | The proposed task can be tackled by leveraging mature tasks such as natural language inference or paraphrase detection. |
Towards Faithful Knowledge Graph Explanation Through Deep Alignment in Commonsense Question Answering (2024.emnlp-main)
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| Challenge: | Current methods for generating faithful explanations overlook path decoding faithfulness, leading to divergence between graph encoder outputs and model predictions. |
| Approach: | They propose an algorithm to assess KG representation reliability and an LM-KG distribution-aware Alignment algorithm to improve explanation faithfulness without ground truth. |
| Outcome: | The proposed algorithm improves explanation faithfulness without ground truth and significantly improves fidelity and model performance. |
Detection and Resolution of Rumors and Misinformation with NLP (2020.coling-tutorials)
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| Challenge: | Detecting false and misleading claims on the web is a sub-field of NLP . this half-day tutorial presents the theory behind each of these steps and the state-of-the-art solutions. |
| Approach: | This half-day tutorial presents the theory behind false and misleading claims detection . it covers the steps involved in identifying check-worthy claims, tracking claims and rumors, rumor collection and annotation, grounding claims against knowledge bases, and using stance to verify claims. |
| Outcome: | This half-day tutorial presents the theory behind each of these steps and the state-of-the-art solutions. |
Entity-aware Cross-lingual Claim Detection for Automated Fact-checking (2026.findings-eacl)
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| Challenge: | Existing work on verifiable claims detection is focused on monolingual solutions . identifying and validating claims related to global concerns requires a fact-checking pipeline capable of processing claims written in multiple languages. |
| Approach: | They propose an entity-aware cross-lingual claim detection model that generalizes well to handle multilingual claims. |
| Outcome: | The proposed model shows consistent performance gains across 27 languages and robust knowledge transfer between languages seen and unseen during training. |
Natural Language Inference with Self-Attention for Veracity Assessment of Pandemic Claims (2022.naacl-main)
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| Challenge: | Existing datasets focus on a single medium, information domain or specific application . authors propose novel methods for automated veracity assessment based on Natural Language Inference . |
| Approach: | They propose to build a PANACEA dataset that combines different data sources with different foci to ensure a unique set of claims. |
| Outcome: | The proposed methods are competitive with SOTA methods and provide a detailed discussion. |
MultiClaimNet: A Massively Multilingual Dataset of Fact-Checked Claim Clusters (2025.findings-emnlp)
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| Challenge: | a growing number of unverified claims and expanding size of fact-checked databases require alternative, more efficient solutions. |
| Approach: | They propose to group fact-checked claims into multilingual clusters to improve claim retrieval and validation. |
| Outcome: | The proposed approach reduces redundancy by grouping claims into clusters . the proposed dataset contains 85.3K fact-checked claims written in 78 languages . |
Active PETs: Active Data Annotation Prioritisation for Few-Shot Claim Verification with Pattern Exploiting Training (2023.findings-eacl)
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| Challenge: | Recent work on few-shot classification has addressed the issue of data prioritisation of unlabelled data. |
| Approach: | They propose a weighted approach that uses a set of pattern-exploiting training models to actively select unlabelled data as candidates for annotation. |
| Outcome: | The proposed approach shows consistent improvement over baseline methods on two technical fact-checking datasets and using six different pretrained language models. |
NLP for Social Good: A Survey and Outlook of Challenges, Opportunities and Responsible Deployment (2026.eacl-long)
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Antonia Karamolegkou, Angana Borah, Eunjung Cho, Sagnik Ray Choudhury, Martina Galletti, Pranav Gupta, Oana Ignat, Priyanka Kargupta, Neema Kotonya, Hemank Lamba, Sun-Joo Lee, Arushi Mangla, Ishani Mondal, Fatima Zahra Moudakir, Deniz Nazar, Poli Nemkova, Dina Pisarevskaya, Naquee Rizwan, Nazanin Sabri, Keenan Samway, Dominik Stammbach, Anna Steinberg Schulten, David Tomás, Steven R Wilson, Bowen Yi, Jessica H Zhu, Arkaitz Zubiaga, Anders Søgaard, Alexander Fraser, Zhijing Jin, Rada Mihalcea, Joel R. Tetreault, Daryna Dementieva
| Challenge: | This paper surveys work in "NLP for Social Good" across nine domains relevant to global development and risk agendas. |
| Approach: | This paper analyzes work in "NLP for Social Good" across nine domains relevant to global development and risk agendas. |
| Outcome: | The paper analyzes work in "NLP for Social Good" across nine domains relevant to global development and risk agendas. |
PANACEA: An Automated Misinformation Detection System on COVID-19 (2023.eacl-demo)
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Runcong Zhao, Miguel Arana-catania, Lixing Zhu, Elena Kochkina, Lin Gui, Arkaitz Zubiaga, Rob Procter, Maria Liakata, Yulan He
| Challenge: | Using social media and fact-checking to detect misinformation is not enough to prevent the spread of false information. |
| Approach: | They propose a web-based misinformation detection system PANACEA which has two modules, fact-checking and rumour detection. |
| Outcome: | The system outperforms state-of-the-art methods and adapts graph convolutional networks model to detect rumours based on tweets rather than knowledge bases. |