Search Results for author: Andreas Vlachos

Found 73 papers, 24 papers with code

Leveraging Wikipedia article evolution for promotional tone detection

1 code implementation ACL 2022 Christine de Kock, Andreas Vlachos

Detecting biased language is useful for a variety of applications, such as identifying hyperpartisan news sources or flagging one-sided rhetoric.

The Fact Extraction and VERification Over Unstructured and Structured information (FEVEROUS) Shared Task

no code implementations EMNLP (FEVER) 2021 Rami Aly, Zhijiang Guo, Michael Sejr Schlichtkrull, James Thorne, Andreas Vlachos, Christos Christodoulopoulos, Oana Cocarascu, Arpit Mittal

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).


AVeriTeC: A Dataset for Real-world Claim Verification with Evidence from the Web

1 code implementation22 May 2023 Michael Schlichtkrull, Zhijiang Guo, Andreas Vlachos

Existing datasets for automated fact-checking have substantial limitations, such as relying on artificial claims, lacking annotations for evidence and intermediate reasoning, or including evidence published after the claim.

Claim Verification Fact Checking +1

The Intended Uses of Automated Fact-Checking Artefacts: Why, How and Who

1 code implementation27 Apr 2023 Michael Schlichtkrull, Nedjma Ousidhoum, Andreas Vlachos

Automated fact-checking is often presented as an epistemic tool that fact-checkers, social media consumers, and other stakeholders can use to fight misinformation.

Fact Checking Misinformation

Opening up Minds with Argumentative Dialogues

no code implementations16 Jan 2023 Youmna Farag, Charlotte O. Brand, Jacopo Amidei, Paul Piwek, Tom Stafford, Svetlana Stoyanchev, Andreas Vlachos

We show that while both models perform closely in terms of opening up minds, the argument-based model is significantly better on other dialogue properties such as engagement and clarity.

How to disagree well: Investigating the dispute tactics used on Wikipedia

1 code implementation16 Dec 2022 Christine de Kock, Tom Stafford, Andreas Vlachos

Disagreements are frequently studied from the perspective of either detecting toxicity or analysing argument structure.

Natural Logic-guided Autoregressive Multi-hop Document Retrieval for Fact Verification

no code implementations10 Dec 2022 Rami Aly, Andreas Vlachos

We propose a novel retrieve-and-rerank method for multi-hop retrieval, that consists of a retriever that jointly scores documents in the knowledge source and sentences from previously retrieved documents using an autoregressive formulation and is guided by a proof system based on natural logic that dynamically terminates the retrieval process if the evidence is deemed sufficient.

Fact Verification Retrieval

Varifocal Question Generation for Fact-checking

1 code implementation22 Oct 2022 Nedjma Ousidhoum, Zhangdie Yuan, Andreas Vlachos

Our method outperforms previous work on a fact-checking question generation dataset on a wide range of automatic evaluation metrics.

Fact Checking Question Answering +2

Improving Scheduled Sampling with Elastic Weight Consolidation for Neural Machine Translation

no code implementations13 Sep 2021 Michalis Korakakis, Andreas Vlachos

In this paper, we conduct systematic experiments and find that scheduled sampling, while it ameliorates exposure bias by increasing model reliance on the input sequence, worsens performance when the prefix at inference time is correct, a form of catastrophic forgetting.

Machine Translation Translation

Cross-Policy Compliance Detection via Question Answering

no code implementations EMNLP 2021 Marzieh Saeidi, Majid Yazdani, Andreas Vlachos

Policy compliance detection is the task of ensuring that a scenario conforms to a policy (e. g. a claim is valid according to government rules or a post in an online platform conforms to community guidelines).

Natural Language Inference Question Answering

A Survey on Automated Fact-Checking

1 code implementation26 Aug 2021 Zhijiang Guo, Michael Schlichtkrull, Andreas Vlachos

Fact-checking has become increasingly important due to the speed with which both information and misinformation can spread in the modern media ecosystem.

Fact Checking Misinformation

ProoFVer: Natural Logic Theorem Proving for Fact Verification

1 code implementation25 Aug 2021 Amrith Krishna, Sebastian Riedel, Andreas Vlachos

Fact verification systems typically rely on neural network classifiers for veracity prediction which lack explainability.

Automated Theorem Proving Decision Making +2

DeliData: A dataset for deliberation in multi-party problem solving

no code implementations11 Aug 2021 Georgi Karadzhov, Tom Stafford, Andreas Vlachos

Group deliberation enables people to collaborate and solve problems, however, it is understudied due to a lack of resources.

Problem-Solving Deliberation

Leveraging Type Descriptions for Zero-shot Named Entity Recognition and Classification

no code implementations ACL 2021 Rami Aly, Andreas Vlachos, Ryan Mcdonald

We address the zero-shot NERC specific challenge that the not-an-entity class is not well defined as different entity classes are considered in training and testing.

Machine Reading Comprehension named-entity-recognition +7

FEVEROUS: Fact Extraction and VERification Over Unstructured and Structured information

1 code implementation10 Jun 2021 Rami Aly, Zhijiang Guo, Michael Schlichtkrull, James Thorne, Andreas Vlachos, Christos Christodoulopoulos, Oana Cocarascu, Arpit Mittal

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.

Fact Verification Misinformation

Evidence-based Factual Error Correction

1 code implementation ACL 2021 James Thorne, Andreas Vlachos

This paper introduces the task of factual error correction: performing edits to a claim so that the generated rewrite is better supported by evidence.

Fact Checking Fact Verification

Web Mining for Estimating Regulatory Blockchain Readiness

no code implementations24 Mar 2021 Elias Iosif, Klitos Christodoulou, Andreas Vlachos

In this work, a computational model is proposed for quantitatively estimating the regulatory stance of countries with respect to cryptocurrencies.


Trajectory-Based Meta-Learning for Out-Of-Vocabulary Word Embedding Learning

no code implementations EACL (AdaptNLP) 2021 Gordon Buck, Andreas Vlachos

Word embedding learning methods require a large number of occurrences of a word to accurately learn its embedding.


Incremental Beam Manipulation for Natural Language Generation

1 code implementation EACL 2021 James Hargreaves, Andreas Vlachos, Guy Emerson

For this reason, it is common to rerank the output of beam search, but this relies on beam search to produce a good set of hypotheses, which limits the potential gains.

Text Generation

I Beg to Differ: A study of constructive disagreement in online conversations

1 code implementation EACL 2021 Christine de Kock, Andreas Vlachos

We develop a variety of neural models and show that taking into account the structure of the conversation improves predictive accuracy, exceeding that of feature-based models.

A Robust Blockchain Readiness Index Model

no code implementations20 Jan 2021 Elias Iosif, Klitos Christodoulou, Andreas Vlachos

As the blockchain ecosystem gets more mature many businesses, investors, and entrepreneurs are seeking opportunities on working with blockchain systems and cryptocurrencies.

Information Retrieval Retrieval

Evidence-based Factual Error Correction

3 code implementations31 Dec 2020 James Thorne, Andreas Vlachos

This paper introduces the task of factual error correction: performing edits to a claim so that the generated rewrite is better supported by evidence.

Fact Checking Fact Verification +1

Generating Fact Checking Briefs

no code implementations EMNLP 2020 Angela Fan, Aleksandra Piktus, Fabio Petroni, Guillaume Wenzek, Marzieh Saeidi, Andreas Vlachos, Antoine Bordes, Sebastian Riedel

Fact checking at scale is difficult -- while the number of active fact checking websites is growing, it remains too small for the needs of the contemporary media ecosystem.

Fact Checking Question Answering

Elastic weight consolidation for better bias inoculation

1 code implementation EACL 2021 James Thorne, Andreas Vlachos

The biases present in training datasets have been shown to affect models for sentence pair classification tasks such as natural language inference (NLI) and fact verification.

Fact Verification General Classification +2

Incorporating Label Dependencies in Multilabel Stance Detection

1 code implementation IJCNLP 2019 William Ferreira, Andreas Vlachos

We propose a method that explicitly incorporates label dependencies in the training objective and compare it against a variety of baselines, as well as a reduction of multilabel to multiclass learning.

General Classification Stance Detection

Neural Generative Rhetorical Structure Parsing

no code implementations IJCNLP 2019 Amandla Mabona, Laura Rimell, Stephen Clark, Andreas Vlachos

We show that, for our parser's traversal order, previous beam search algorithms for RNNGs have a left-branching bias which is ill-suited for RST parsing.

Document Classification

Meta-Learning Improves Lifelong Relation Extraction

no code implementations WS 2019 Abiola Obamuyide, Andreas Vlachos

Most existing relation extraction models assume a fixed set of relations and are unable to adapt to exploit newly available supervision data to extract new relations.

Meta-Learning Relation Extraction

HighRES: Highlight-based Reference-less Evaluation of Summarization

1 code implementation ACL 2019 Hardy, Shashi Narayan, Andreas Vlachos

There has been substantial progress in summarization research enabled by the availability of novel, often large-scale, datasets and recent advances on neural network-based approaches.

Generating Token-Level Explanations for Natural Language Inference

no code implementations NAACL 2019 James Thorne, Andreas Vlachos, Christos Christodoulopoulos, Arpit Mittal

In this paper, we show that it is possible to generate token-level explanations for NLI without the need for training data explicitly annotated for this purpose.

Multiple Instance Learning Natural Language Inference +1

Automated Fact Checking in the News Room

no code implementations3 Apr 2019 Sebastião Miranda, David Nogueira, Afonso Mendes, Andreas Vlachos, Andrew Secker, Rebecca Garrett, Jeff Mitchel, Zita Marinho

Fact checking is an essential task in journalism; its importance has been highlighted due to recently increased concerns and efforts in combating misinformation.

Fact Checking Misinformation

Adversarial attacks against Fact Extraction and VERification

no code implementations13 Mar 2019 James Thorne, Andreas Vlachos

This paper describes a baseline for the second iteration of the Fact Extraction and VERification shared task (FEVER2. 0) which explores the resilience of systems through adversarial evaluation.

Fact Checking Information Retrieval +2

Guided Neural Language Generation for Abstractive Summarization using Abstract Meaning Representation

1 code implementation EMNLP 2018 Hardy, Andreas Vlachos

In this paper, we extend previous work on abstractive summarization using Abstract Meaning Representation (AMR) with a neural language generation stage which we guide using the source document.

Abstractive Text Summarization Text Generation

Topic or Style? Exploring the Most Useful Features for Authorship Attribution

1 code implementation COLING 2018 Yunita Sari, Mark Stevenson, Andreas Vlachos

Approaches to authorship attribution, the task of identifying the author of a document, are based on analysis of individuals{'} writing style and/or preferred topics.

Text Categorization

Automated Fact Checking: Task formulations, methods and future directions

no code implementations COLING 2018 James Thorne, Andreas Vlachos

The recently increased focus on misinformation has stimulated research in fact checking, the task of assessing the truthfulness of a claim.

Fact Checking Misinformation

FEVER: a large-scale dataset for Fact Extraction and VERification

5 code implementations NAACL 2018 James Thorne, Andreas Vlachos, Christos Christodoulopoulos, Arpit Mittal

Thus we believe that FEVER is a challenging testbed that will help stimulate progress on claim verification against textual sources.

Claim Verification

Sheffield at SemEval-2017 Task 9: Transition-based language generation from AMR.

no code implementations SEMEVAL 2017 Gerasimos Lampouras, Andreas Vlachos

This paper describes the submission by the University of Sheffield to the SemEval 2017 Abstract Meaning Representation Parsing and Generation task (SemEval 2017 Task 9, Subtask 2).

Language Modelling Machine Translation +1

Imitation learning for structured prediction in natural language processing

no code implementations EACL 2017 Andreas Vlachos, Gerasimos Lampouras, Sebastian Riedel

Imitation learning is a learning paradigm originally developed to learn robotic controllers from demonstrations by humans, e. g. autonomous flight from pilot demonstrations.

coreference-resolution Dependency Parsing +5

An Extensible Framework for Verification of Numerical Claims

no code implementations EACL 2017 James Thorne, Andreas Vlachos

In this paper we present our automated fact checking system demonstration which we developed in order to participate in the Fast and Furious Fact Check challenge.

Fact Checking Rumour Detection +1

Stance Detection with Bidirectional Conditional Encoding

1 code implementation EMNLP 2016 Isabelle Augenstein, Tim Rocktäschel, Andreas Vlachos, Kalina Bontcheva

Stance detection is the task of classifying the attitude expressed in a text towards a target such as Hillary Clinton to be "positive", negative" or "neutral".

Stance Detection

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