13 citations · 36 across the 17 of their papers we have counts for
20 papers
SemEval-2025 Task 11: Bridging the Gap in Text-Based Emotion Detection
Shamsuddeen Hassan Muhammad, Nedjma Ousidhoum, Idris Abdulmumin +18
We present our shared task on text-based emotion detection, covering more than 30 languages from seven distinct language families. These languages are predominantly low-resource an…
BRIGHTER: BRIdging the Gap in Human-Annotated Textual Emotion Recognition Datasets for 28 Languages
Shamsuddeen Hassan Muhammad, Nedjma Ousidhoum, Idris Abdulmumin +45
People worldwide use language in subtle and complex ways to express emotions. Although emotion recognition--an umbrella term for several NLP tasks--impacts various applications wit…
Extracting and Encoding: Leveraging Large Language Models and Medical Knowledge to Enhance Radiological Text Representation
Pablo Messina, René Vidal, Denis Parra +2
Advancing representation learning in specialized fields like medicine remains challenging due to the scarcity of expert annotations for text and images. To tackle this issue, we pr…
CVQA: Culturally-diverse Multilingual Visual Question Answering Benchmark
David Romero, Chenyang Lyu, Haryo Akbarianto Wibowo +73
Visual Question Answering (VQA) is an important task in multimodal AI, and it is often used to test the ability of vision-language models to understand and reason on knowledge pres…
SemEval-2024 Task 1: Semantic Textual Relatedness for African and Asian Languages
Nedjma Ousidhoum, Shamsuddeen Hassan Muhammad, Mohamed Abdalla +14
We present the first shared task on Semantic Textual Relatedness (STR). While earlier shared tasks primarily focused on semantic similarity, we instead investigate the broader phen…
SemRel2024: A Collection of Semantic Textual Relatedness Datasets for 13 Languages
Nedjma Ousidhoum, Shamsuddeen Hassan Muhammad, Mohamed Abdalla +24
Exploring and quantifying semantic relatedness is central to representing language and holds significant implications across various NLP tasks. While earlier NLP research primarily…
A Memory Model for Question Answering from Streaming Data Supported by Rehearsal and Anticipation of Coreference Information
Vladimir Araujo, Alvaro Soto, Marie-Francine Moens
Existing question answering methods often assume that the input content (e.g., documents or videos) is always accessible to solve the task. Alternatively, memory networks were intr…
How Relevant is Selective Memory Population in Lifelong Language Learning?
Vladimir Araujo, Helena Balabin, Julio Hurtado +2
Lifelong language learning seeks to have models continuously learn multiple tasks in a sequential order without suffering from catastrophic forgetting. State-of-the-art approaches…
Memory Population in Continual Learning via Outlier Elimination
Julio Hurtado, Alain Raymond-Saez, Vladimir Araujo +3
Catastrophic forgetting, the phenomenon of forgetting previously learned tasks when learning a new one, is a major hurdle in developing continual learning algorithms. A popular met…
Entropy-based Stability-Plasticity for Lifelong Learning
Vladimir Araujo, Julio Hurtado, Alvaro Soto +1
The ability to continuously learn remains elusive for deep learning models. Unlike humans, models cannot accumulate knowledge in their weights when learning new tasks, mainly due t…
Evaluation Benchmarks for Spanish Sentence Representations
Vladimir Araujo, Andrés Carvallo, Souvik Kundu +6
Due to the success of pre-trained language models, versions of languages other than English have been released in recent years. This fact implies the need for resources to evaluate…
ALBETO and DistilBETO: Lightweight Spanish Language Models
José Cañete, Sebastián Donoso, Felipe Bravo-Marquez +2
In recent years there have been considerable advances in pre-trained language models, where non-English language versions have also been made available. Due to their increasing use…
DACT-BERT: Differentiable Adaptive Computation Time for an Efficient BERT Inference
Cristóbal Eyzaguirre, Felipe del Río, Vladimir Araujo +1
Large-scale pre-trained language models have shown remarkable results in diverse NLP applications. Unfortunately, these performance gains have been accompanied by a significant inc…
Augmenting BERT-style Models with Predictive Coding to Improve Discourse-level Representations
Vladimir Araujo, Andrés Villa, Marcelo Mendoza +2
Current language models are usually trained using a self-supervised scheme, where the main focus is learning representations at the word or sentence level. However, there has been…
Stress Test Evaluation of Biomedical Word Embeddings
Vladimir Araujo, Andrés Carvallo, Carlos Aspillaga +2
The success of pretrained word embeddings has motivated their use in the biomedical domain, with contextualized embeddings yielding remarkable results in several biomedical NLP tas…
TNT: Text-Conditioned Network with Transductive Inference for Few-Shot Video Classification
Andrés Villa, Juan-Manuel Perez-Rua, Victor Escorcia +3
Recently, few-shot video classification has received an increasing interest. Current approaches mostly focus on effectively exploiting the temporal dimension in videos to improve l…
Interpretable Contextual Team-aware Item Recommendation: Application in Multiplayer Online Battle Arena Games
Andrés Villa, Vladimir Araujo, Francisca Cattan +1
The video game industry has adopted recommendation systems to boost users interest with a focus on game sales. Other exciting applications within video games are those that help th…
Translating Natural Language Instructions for Behavioral Robot Navigation with a Multi-Head Attention Mechanism
Patricio Cerda-Mardini, Vladimir Araujo, Alvaro Soto
We propose a multi-head attention mechanism as a blending layer in a neural network model that translates natural language to a high level behavioral language for indoor robot navi…
On Adversarial Examples for Biomedical NLP Tasks
Vladimir Araujo, Andres Carvallo, Carlos Aspillaga +1
The success of pre-trained word embeddings has motivated its use in tasks in the biomedical domain. The BERT language model has shown remarkable results on standard performance met…
Stress Test Evaluation of Transformer-based Models in Natural Language Understanding Tasks
Carlos Aspillaga, Andrés Carvallo, Vladimir Araujo
There has been significant progress in recent years in the field of Natural Language Processing thanks to the introduction of the Transformer architecture. Current state-of-the-art…