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Found 11 papers, 5 papers with code
MOMENT: A Family of Open Time-series Foundation Models
Pre-training large models on time-series data is challenging due to (1) the absence of a large and cohesive public time-series repository, and (2) diverse time-series characteristics which make multi-dataset training onerous.
AQuA: A Benchmarking Tool for Label Quality Assessment
We hope that our proposed design space and benchmark enable practitioners to choose the right tools to improve their label quality and that our benchmark enables objective and rigorous evaluation of machine learning tools facing mislabeled data.
Adversarial Adaptation for French Named Entity Recognition
We propose a Transformer-based NER approach for French, using adversarial adaptation to similar domain or general corpora to improve feature extraction and enable better generalization.
Transformer-Based Named Entity Recognition for French Using Adversarial Adaptation to Similar Domain Corpora
Named Entity Recognition (NER) involves the identification and classification of named entities in unstructured text into predefined classes.
An Emotion-guided Approach to Domain Adaptive Fake News Detection using Adversarial Learning
Recent works on fake news detection have shown the efficacy of using emotions as a feature for improved performance.
Emotion-guided Cross-domain Fake News Detection using Adversarial Domain Adaptation
Recent works on fake news detection have shown the efficacy of using emotions as a feature or emotions-based features for improved performance.
An Emotion-Aware Multi-Task Approach to Fake News and Rumour Detection using Transfer Learning
We further analyze the performance of our multi-task approach for fake news detection in cross-domain settings to verify its efficacy for better generalization across datasets, and to verify that emotions act as a domain-independent feature.
A Spreader Ranking Algorithm for Extremely Low-budget Influence Maximization in Social Networks using Community Bridge Nodes
In this work, we propose a community structures-based approach, which employs a K-Shell algorithm in order to generate a score for the connections between seed nodes and communities for low-budget scenarios.
Adaptation of domain-specific transformer models with text oversampling for sentiment analysis of social media posts on Covid-19 vaccines
Covid-19 has spread across the world and several vaccines have been developed to counter its surge.
TLMOTE: A Topic-based Language Modelling Approach for Text Oversampling
To tackle the problem of bias towards majority classes, researchers have presented various techniques to oversample the minority class data points.
Once Upon A Time In Visualization: Understanding the Use of Textual Narratives for Causality
In this paper, we propose the use of textual narratives as a data-driven storytelling method to augment causality visualization.
