Search Results for author: Christos Christodoulopoulos

Found 25 papers, 5 papers with code

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).

Debiasing knowledge graph embeddings

no code implementations EMNLP 2020 Joseph Fisher, Arpit Mittal, Dave Palfrey, Christos Christodoulopoulos

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.

Knowledge Graph Embeddings

Robust Information Retrieval for False Claims with Distracting Entities In Fact Extraction and Verification

no code implementations10 Dec 2021 Mingwen Dong, Christos Christodoulopoulos, Sheng-Min Shih, Xiaofei Ma

A BERT-based retrieval model made more mistakes in retrieving refuting evidence for false claims than supporting evidence for true claims.

Data Augmentation Fact Checking +1

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

Hidden Biases in Unreliable News Detection Datasets

no code implementations EACL 2021 Xiang Zhou, Heba Elfardy, Christos Christodoulopoulos, Thomas Butler, Mohit Bansal

Using the observations and experimental results, we provide practical suggestions on how to create more reliable datasets for the unreliable news detection task.

Fact Checking Selection bias

Measuring Social Bias in Knowledge Graph Embeddings

no code implementations5 Dec 2019 Joseph Fisher, Dave Palfrey, Christos Christodoulopoulos, Arpit Mittal

It has recently been shown that word embeddings encode social biases, with a harmful impact on downstream tasks.

Knowledge Graph Embeddings Word Embeddings

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

Simple Large-scale Relation Extraction from Unstructured Text

no code implementations LREC 2018 Christos Christodoulopoulos, Arpit Mittal

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).

Question Answering Relation Extraction

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.

Relational Learning and Feature Extraction by Querying over Heterogeneous Information Networks

no code implementations25 Jul 2017 Parisa Kordjamshidi, Sameer Singh, Daniel Khashabi, Christos Christodoulopoulos, Mark Summons, Saurabh Sinha, Dan Roth

In particular, we provide an initial prototype for a relational and graph traversal query language where queries are directly used as relational features for structured machine learning models.

Knowledge Graphs Relational Reasoning

Better call Saul: Flexible Programming for Learning and Inference in NLP

1 code implementation COLING 2016 Parisa Kordjamshidi, Daniel Khashabi, Christos Christodoulopoulos, Bhargav Mangipudi, Sameer Singh, Dan Roth

We present a novel way for designing complex joint inference and learning models using Saul (Kordjamshidi et al., 2015), a recently-introduced declarative learning-based programming language (DeLBP).

Part-Of-Speech Tagging Probabilistic Programming +1

Transliteration in Any Language with Surrogate Languages

no code implementations14 Sep 2016 Stephen Mayhew, Christos Christodoulopoulos, Dan Roth

We introduce a method for transliteration generation that can produce transliterations in every language.


EDISON: Feature Extraction for NLP, Simplified

no code implementations LREC 2016 Mark Sammons, Christos Christodoulopoulos, Parisa Kordjamshidi, Daniel Khashabi, Vivek Srikumar, Dan Roth

We present EDISON, a Java library of feature generation functions used in a suite of state-of-the-art NLP tools, based on a set of generic NLP data structures.

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