Search Results for author:
Found 30 papers, 8 papers with code
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, 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).
A Relation Extraction Dataset for Knowledge Extraction from Web Tables
Most datasets are constructed using synthetic tables that lack valuable metadata information, or are limited in size to be considered as a challenging evaluation set.
Debiasing knowledge graph embeddings
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.
The Automated Verification of Textual Claims (AVeriTeC) Shared Task
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.
WebIE: Faithful and Robust Information Extraction on the Web
We evaluate the in-domain, out-of-domain, and zero-shot cross-lingual performance of generative IE models and find models trained on WebIE show better generalisability.
State-of-the-art generalisation research in NLP: A taxonomy and review
We present a taxonomy for characterising and understanding generalisation research in NLP.
ReFinED: An Efficient Zero-shot-capable Approach to End-to-End Entity Linking
The model is capable of generalising to large-scale knowledge bases such as Wikidata (which has 15 times more entities than Wikipedia) and of zero-shot entity linking.
Ranked #1 on
Entity Linking
on WebQSP-WD
(using extra training data)
Robust Information Retrieval for False Claims with Distracting Entities In Fact Extraction and Verification
A BERT-based retrieval model made more mistakes in retrieving refuting evidence for false claims than supporting evidence for true claims.
FEVEROUS: Fact Extraction and VERification Over Unstructured and Structured information
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.
Hidden Biases in Unreliable News Detection Datasets
Using the observations and experimental results, we provide practical suggestions on how to create more reliable datasets for the unreliable news detection task.
Measuring Social Bias in Knowledge Graph Embeddings
It has recently been shown that word embeddings encode social biases, with a harmful impact on downstream tasks.
Evaluating adversarial attacks against multiple fact verification systems
Automated fact verification has been progressing owing to advancements in modeling and availability of large datasets.
The FEVER2.0 Shared Task
We present the results of the second Fact Extraction and VERification (FEVER2. 0) Shared Task.
Generating Token-Level Explanations for Natural Language Inference
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.
The Fact Extraction and VERification (FEVER) Shared Task
We present the results of the first Fact Extraction and VERification (FEVER) Shared Task.
Gold Standard Annotations for Preposition and Verb Sense with Semantic Role Labels in Adult-Child Interactions
Inter-annotator agreement is given separately for prepositions and verbs, and for adult speech and child speech.
Knowledge Representation and Extraction at Scale
Finally, I will present the Fact Extraction and VERification (FEVER) dataset and challenge.
Simple Large-scale Relation Extraction from Unstructured Text
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).
FEVER: a large-scale dataset for Fact Extraction and VERification
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
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.
Better call Saul: Flexible Programming for Learning and Inference in NLP
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).
Revisiting the Evaluation for Cross Document Event Coreference
Cross document event coreference (CDEC) is an important task that aims at aggregating event-related information across multiple documents.
Transliteration in Any Language with Surrogate Languages
We introduce a method for transliteration generation that can produce transliterations in every language.
EDISON: Feature Extraction for NLP, Simplified
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.
