Search Results for author:
Found 11 papers, 4 papers with code
AIDA-UPM at SemEval-2022 Task 5: Exploring Multimodal Late Information Fusion for Multimedia Automatic Misogyny Identification
The main contribution of this paper is the exploration of different late fusion methods to boost the performance of the combination based on the Transformer-based model and Convolutional Neural Networks (CNN) for text and image, respectively.
Camouflage is all you need: Evaluating and Enhancing Language Model Robustness Against Camouflage Adversarial Attacks
This approach effectively reduces the performance drop in encoder-only models to an average of 5% in offensive language detection and 2% in misinformation detection tasks.
Understanding writing style in social media with a supervised contrastively pre-trained transformer
Additionally, we attain promising results on PAN verification challenges using a single dense layer, with our model serving as an embedding encoder.
Spain on Fire: A novel wildfire risk assessment model based on image satellite processing and atmospheric information
Each year, wildfires destroy larger areas of Spain, threatening numerous ecosystems.
PART: Pre-trained Authorship Representation Transformer
The model is evaluated on these datasets, achieving zero-shot 72. 39\% and 86. 73\% accuracy and top-5 accuracy respectively on the joint evaluation dataset when determining authorship from a set of 250 different authors.
Deep learning for understanding multilabel imbalanced Chest X-ray datasets
To apply these algorithms in different fields and test how the methodology works, we need to use eXplainable AI techniques.
Exploring Dimensionality Reduction Techniques in Multilingual Transformers
The results of this study will significantly contribute to the understanding of how different tuning approaches affect performance on semantic-aware tasks and how dimensional reduction techniques deal with the high-dimensional embeddings computed for the STS task and their potential for highly demanding NLP tasks
BERTuit: Understanding Spanish language in Twitter through a native transformer
Our motivation is to provide a powerful resource to better understand Spanish Twitter and to be used on applications focused on this social network, with special emphasis on solutions devoted to tackle the spreading of misinformation in this platform.
FacTeR-Check: Semi-automated fact-checking through Semantic Similarity and Natural Language Inference
Our society produces and shares overwhelming amounts of information through Online Social Networks (OSNs).
SILT: Efficient transformer training for inter-lingual inference
In this paper, we propose a new architecture called Siamese Inter-Lingual Transformer (SILT), to efficiently align multilingual embeddings for Natural Language Inference, allowing for unmatched language pairs to be processed.
Fusing CNNs and statistical indicators to improve image classification
In this paper, an ensemble method is proposed for accurate image classification, fusing automatically detected features through Convolutional Neural Network architectures with a set of manually defined statistical indicators.
