Christos Christodoulopoulos
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View article: The Automated Verification of Textual Claims (AVeriTeC) Shared Task
The Automated Verification of Textual Claims (AVeriTeC) Shared Task Open
The Automated Verification of Textual Claims (AVeriTeC) shared task asks participants to retrieve evidence and predict veracity for real-world claims checked by fact-checkers. Evidence can be found either via a search engine, or via a know…
View article: A taxonomy and review of generalization research in NLP
A taxonomy and review of generalization research in NLP Open
The ability to generalize well is one of the primary desiderata for models of natural language processing (NLP), but what ‘good generalization’ entails and how it should be evaluated is not well understood. In this Analysis we present a ta…
View article: A taxonomy and review of generalization research in NLP
A taxonomy and review of generalization research in NLP Open
The ability to generalise well is one of the primary desiderata of natural language processing (NLP). Yet, what 'good generalisation' entails and how it should be evaluated is not well understood, nor are there any evaluation standards for…
View article: WebIE: Faithful and Robust Information Extraction on the Web
WebIE: Faithful and Robust Information Extraction on the Web Open
Extracting structured and grounded fact triples from raw text is a fundamental task in Information Extraction (IE). Existing IE datasets are typically collected from Wikipedia articles, using hyperlinks to link entities to the Wikidata kno…
View article: WebIE: Faithful and Robust Information Extraction on the Web
WebIE: Faithful and Robust Information Extraction on the Web Open
Chenxi Whitehouse, Clara Vania, Alham Fikri Aji, Christos Christodoulopoulos, Andrea Pierleoni. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2023.
View article: mReFinED: An Efficient End-to-End Multilingual Entity Linking System
mReFinED: An Efficient End-to-End Multilingual Entity Linking System Open
End-to-end multilingual entity linking (MEL) is concerned with identifying multilingual entity mentions and their corresponding entity IDs in a knowledge base. Existing works assumed that entity mentions were given and skipped the entity m…
View article: ReFinED: An Efficient Zero-shot-capable Approach to End-to-End Entity Linking
ReFinED: An Efficient Zero-shot-capable Approach to End-to-End Entity Linking Open
We introduce ReFinED, an efficient end-to-end entity linking model which uses fine-grained entity types and entity descriptions to perform linking. The model performs mention detection, fine-grained entity typing, and entity disambiguation…
View article: ReFinED: An Efficient Zero-shot-capable Approach to End-to-End Entity Linking
ReFinED: An Efficient Zero-shot-capable Approach to End-to-End Entity Linking Open
Tom Ayoola, Shubhi Tyagi, Joseph Fisher, Christos Christodoulopoulos, Andrea Pierleoni. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industr…
View article: Robust Information Retrieval for False Claims with Distracting Entities In Fact Extraction and Verification
Robust Information Retrieval for False Claims with Distracting Entities In Fact Extraction and Verification Open
Accurate evidence retrieval is essential for automated fact checking. Little previous research has focused on the differences between true and false claims and how they affect evidence retrieval. This paper shows that, compared with true c…
View article: FEVEROUS: Fact Extraction and VERification Over Unstructured and Structured information
FEVEROUS: Fact Extraction and VERification Over Unstructured and Structured information Open
Fact verification has attracted a lot of attention in the machine learning and natural language processing communities, as it is one of the key methods for detecting misinformation. Existing large-scale benchmarks for this task have focuse…
View article: FEVEROUS: Fact Extraction and VERification Over Unstructured and Structured information
FEVEROUS: Fact Extraction and VERification Over Unstructured and Structured information Open
FEVEROUS (Fact Extraction and VERification Over Unstructured and Structured information) is a fact verification dataset which consists of 87,026 verified claims. Each claim is annotated with evidence in the form of sentences and/or cells f…
View article: FEVEROUS: Fact Extraction and VERification Over Unstructured and Structured information
FEVEROUS: Fact Extraction and VERification Over Unstructured and Structured information Open
FEVEROUS (Fact Extraction and VERification Over Unstructured and Structured information) is a fact verification dataset which consists of 87,026 verified claims. Each claim is annotated with evidence in the form of sentences and/or cells f…
View article: Hidden Biases in Unreliable News Detection Datasets
Hidden Biases in Unreliable News Detection Datasets Open
Automatic unreliable news detection is a research problem with great potential impact. Recently, several papers have shown promising results on large-scale news datasets with models that only use the article itself without resorting to any…
View article: Hidden Biases in Unreliable News Detection Datasets
Hidden Biases in Unreliable News Detection Datasets Open
Xiang Zhou, Heba Elfardy, Christos Christodoulopoulos, Thomas Butler, Mohit Bansal. Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume. 2021.
View article: The Fact Extraction and VERification Over Unstructured and Structured information (FEVEROUS) Shared Task
The Fact Extraction and VERification Over Unstructured and Structured information (FEVEROUS) Shared Task Open
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). 2021.
View article: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations Open
View article: Debiasing knowledge graph embeddings
Debiasing knowledge graph embeddings Open
It has been shown that knowledge graph embeddings encode potentially harmful social biases, such as the information that women are more likely to be nurses, and men more likely to be bankers. As graph embeddings begin to be used more widel…
View article: Measuring Social Bias in Knowledge Graph Embeddings
Measuring Social Bias in Knowledge Graph Embeddings Open
It has recently been shown that word embeddings encode social biases, with a harmful impact on downstream tasks. However, to this point there has been no similar work done in the field of graph embeddings. We present the first study on soc…
View article: Generating Token-Level Explanations for Natural Language Inference
Generating Token-Level Explanations for Natural Language Inference Open
The task of Natural Language Inference (NLI) is widely modeled as supervised sentence pair classification. While there has been a lot of work recently on generating explanations of the predictions of classifiers on a single piece of text, …
View article: Generating Token-Level Explanations for Natural Language Inference
Generating Token-Level Explanations for Natural Language Inference Open
James Thorne, Andreas Vlachos, Christos Christodoulopoulos, Arpit Mittal. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Sh…
View article: The FEVER2.0 Shared Task
The FEVER2.0 Shared Task Open
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 adversari…
View article: Evaluating adversarial attacks against multiple fact verification systems
Evaluating adversarial attacks against multiple fact verification systems Open
© 2019 Association for Computational Linguistics Automated fact verification has been progressing owing to advancements in modeling and availability of large datasets. Due to the nature of the task, it is critical to understand the vulnera…
View article: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations Open
The ACL 2019 demonstrations track invites submissions ranging from early research prototypes to mature production-ready systems.We received 100 submissions this year, of which 34 were selected for inclusion in the program (acceptance rate …
View article: FEVER: Fact Extraction and VERification
FEVER: Fact Extraction and VERification Open
FEVER (Fact Extraction and VERification) consists of 185,445 claims generated by altering sentences extracted from Wikipedia and subsequently verified without knowledge of the sentence they were derived from. The claims are classified as <…
View article: FEVER: Fact Extraction and VERification
FEVER: Fact Extraction and VERification Open
FEVER (Fact Extraction and VERification) consists of 185,445 claims generated by altering sentences extracted from Wikipedia and subsequently verified without knowledge of the sentence they were derived from. The claims are classified as <…
View article: Simple Large-scale Relation Extraction from Unstructured Text
Simple Large-scale Relation Extraction from Unstructured Text Open
Knowledge-based question answering relies on the availability of facts, the majority of which cannot be found in structured sources (e.g. Wikipedia info-boxes, Wikidata). One of the major components of extracting facts from unstructured te…
View article: FEVER: a Large-scale Dataset for Fact Extraction and VERification
FEVER: a Large-scale Dataset for Fact Extraction and VERification Open
In this paper we introduce a new publicly available dataset for verification against textual sources, FEVER: Fact Extraction and VERification. It consists of 185,445 claims generated by altering sentences extracted from Wikipedia and subse…