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Found 15 papers, 6 papers with code
Neural Machine Translation for Fact-checking Temporal Claims
Computational fact-checking aims at supporting the verification process of textual claims by exploiting trustworthy sources.
Automatic Verification of Data Summaries
We present a generic method to compute thefactual accuracy of a generated data summarywith minimal user effort.
Neural Re-rankers for Evidence Retrieval in the FEVEROUS Task
Computational fact-checking has gained a lot of traction in the machine learning and natural language processing communities.
Retrieve, Merge, Predict: Augmenting Tables with Data Lakes
We present an in-depth analysis of data discovery in data lakes, focusing on table augmentation for given machine learning tasks.
Variable Selection in Maximum Mean Discrepancy for Interpretable Distribution Comparison
For this optimisation, we introduce sparse regularisation and propose two methods for dealing with the issue of selecting an appropriate regularisation parameter.
Querying Large Language Models with SQL
Thus, we envision the use of SQL queries to cover a broad range of data that is not captured by traditional databases by tapping the information in LLMs.
Crowdsourced Fact-Checking at Twitter: How Does the Crowd Compare With Experts?
Fact-checking is one of the effective solutions in fighting online misinformation.
Unsupervised Matching of Data and Text
Our method builds a fine-grained graph over the content of the corpora and derives word embeddings to represent the objects to match in a low dimensional space.
RuleBert: Teaching Soft Rules to Pre-trained Language Models
While pre-trained language models (PLMs) are the go-to solution to tackle many natural language processing problems, they are still very limited in their ability to capture and to use common-sense knowledge.
Automated Fact-Checking for Assisting Human Fact-Checkers
The reporting and the analysis of current events around the globe has expanded from professional, editor-lead journalism all the way to citizen journalism.
LIBRE: Learning Interpretable Boolean Rule Ensembles
We present a novel method - LIBRE - to learn an interpretable classifier, which materializes as a set of Boolean rules.
Local Embeddings for Relational Data Integration
The embeddings are learned based on such sentences.
Explainable Fact Checking with Probabilistic Answer Set Programming
One challenge in fact checking is the ability to improve the transparency of the decision.
Robust Discovery of Positive and Negative Rules in Knowledge-Bases
We present RUDIK, a system for the discovery of declarative rules over knowledge-bases (KBs).
Query-limited Black-box Attacks to Classifiers
Specifically, we consider the problem of attacking machine learning classifiers subject to a budget of feature modification cost while minimizing the number of queries, where each query returns only a class and confidence score.
