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Vladimir Araujo

10 accepted papers

2025

BRIGHTER: BRIdging the Gap in Human-Annotated Textual Emotion Recognition Datasets for 28 Languages

Shamsuddeen Hassan Muhammad, Nedjma Ousidhoum, Idris Abdulmumin, Jan Philip Wahle, Terry Ruas, Meriem Beloucif +42
ACL 2025long

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 within NLP and beyond, most work in this area has focused on high-resource languages. This has led to significant disparities…

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2025

CaMMT: Benchmarking Culturally Aware Multimodal Machine Translation

Emilio Villa-Cueva, Sholpan Bolatzhanova, Diana Turmakhan, Kareem Elzeky, Henok Biadglign Ademtew, Alham Fikri Aji +29
EMNLP 2025

Translating cultural content poses challenges for machine translation systems due to the differences in conceptualizations between cultures, where language alone may fail to convey sufficient context to capture region-specific meanings. In this work, we investigate whether images can act as cultural

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2024

CVQA: Culturally-diverse Multilingual Visual Question Answering Benchmark

David Orlando Romero Mogrovejo, Chenyang Lyu, Haryo Akbarianto Wibowo, Santiago Góngora, Aishik Mandal, Sukannya Purkayastha +70
NeurIPS 2024oral

Visual Question Answering~(VQA) is an important task in multimodal AI, which requires models to understand and reason on knowledge present in visual and textual data. However, most of the current VQA datasets and models are primarily focused on English and a few major world languages, with images th…

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2024

Extracting and Encoding: Leveraging Large Language Models and Medical Knowledge to Enhance Radiological Text Representation

Pablo Messina, Rene Vidal, Denis Parra, Alvaro Soto, Vladimir Araujo
ACL 2024findings

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 present a novel two-stage framework designed to extract high-quality factual statements from free-text radiology reports i…

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2024

Learning to Route for Dynamic Adapter Composition in Continual Learning with Language Models

Vladimir Araujo, Marie-Francine Moens, Tinne Tuytelaars
EMNLP 2024finding

Parameter-efficient fine-tuning (PEFT) methods are increasingly used with pre-trained language models (PLMs) for continual learning (CL). These methods typically involve training a PEFT module for each new task and employing similarity-based selection to route modules during inference. However, they…

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2024

Pixology: Probing the Linguistic and Visual Capabilities of Pixel-based Language Models

Kushal Tatariya, Vladimir Araujo, Thomas Bauwens, Miryam de Lhoneux
EMNLP 2024main

Pixel-based language models have emerged as a compelling alternative to subword-based language modelling, particularly because they can represent virtually any script. PIXEL, a canonical example of such a model, is a vision transformer that has been pre-trained on rendered text. While PIXEL has show…

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2024

SemRel2024: A Collection of Semantic Textual Relatedness Datasets for 13 Languages

Nedjma Ousidhoum, Shamsuddeen Muhammad, Mohamed Abdalla, Idris Abdulmumin, Ibrahim Ahmad, Sanchit Ahuja +21
ACL 2024findings

Exploring and quantifying semantic relatedness is central to representing language and holds significant implications across various NLP tasks. While earlier NLP research primarily focused on semantic similarity, often within the English language context, we instead investigate the broader phenomeno…

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2024

Sequence-to-Sequence Spanish Pre-trained Language Models

Vladimir Araujo, Maria Mihaela Trusca, Rodrigo Tufiño, Marie-Francine Moens
COLING 2024main

In recent years, significant advancements in pre-trained language models have driven the creation of numerous non-English language variants, with a particular emphasis on encoder-only and decoder-only architectures. While Spanish language models based on BERT and GPT have demonstrated proficiency in…

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2023

A Memory Model for Question Answering from Streaming Data Supported by Rehearsal and Anticipation of Coreference Information

Vladimir Araujo, Alvaro Soto, Marie-Francine Moens
ACL 2023findings

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 introduced to mimic the human process of incremental comprehension and compression of the information in a fixed-capacity me…

Cited by 6Source ↗PDF ↗Scholar ↗
2021

Augmenting BERT-style Models with Predictive Coding to Improve Discourse-level Representations

Vladimir Araujo, Andrés Villa, Marcelo Mendoza, Marie-Francine Moens, Alvaro Soto
EMNLP 2021main

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 limited progress in generating useful discourse-level representations. In this work, we propose to use ideas from predic…

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