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Found 17 papers, 2 papers with code
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 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).
CHIP: Contrastive Hierarchical Image Pretraining
Few-shot object classification is the task of classifying objects in an image with limited number of examples as supervision.
Emotion-Cause Pair Extraction in Customer Reviews
Emotion-Cause Pair Extraction (ECPE) is a complex yet popular area in Natural Language Processing due to its importance and potential applications in various domains.
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.
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.
The FEVER2.0 Shared Task
We present the results of the second Fact Extraction and VERification (FEVER2. 0) Shared Task.
Evaluating adversarial attacks against multiple fact verification systems
Automated fact verification has been progressing owing to advancements in modeling and availability of large datasets.
Using Pairwise Occurrence Information to Improve Knowledge Graph Completion on Large-Scale Datasets
Bilinear models such as DistMult and ComplEx are effective methods for knowledge graph (KG) completion.
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Large Scale Question Paraphrase Retrieval with Smoothed Deep Metric Learning
The goal of a Question Paraphrase Retrieval (QPR) system is to retrieve equivalent questions that result in the same answer as the original question.
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.
Learning When Not to Answer: A Ternary Reward Structure for Reinforcement Learning based Question Answering
In this paper, we investigate the challenges of using reinforcement learning agents for question-answering over knowledge graphs for real-world applications.
The Fact Extraction and VERification (FEVER) Shared Task
We present the results of the first Fact Extraction and VERification (FEVER) Shared Task.
Demand-Weighted Completeness Prediction for a Knowledge Base
In this paper we introduce the notion of Demand-Weighted Completeness, allowing estimation of the completeness of a knowledge base with respect to how it is used.
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.
Labeling Topics with Images using Neural Networks
Topics generated by topic models are usually represented by lists of $t$ terms or alternatively using short phrases and images.
