7 citations · 14 across the 16 of their papers we have counts for
17 papers
Knowledge-Graph Based Augmentation versus Retrieval Augmented Generation for Cultural-Related Question Answering
Pablo Poulenard, Yannis Karmim, Valentin Barrière
Large language models (LLMs) suffer from a long-tail deficit: culturally specific facts, particularly those concerning underrepresented regions such as Latin America, appear too ra…
Multi-Hop Knowledge Composition is Bound by Pretraining Exposure
Yannis Karmim, Luis Marti, Djamé Seddah +1
Large Language Models fail at implicit multi-hop reasoning: a model answers "When was born?" and "Who is 's closest friend?" correctly but fails on "When was 's closest f…
MultiLinguahah : A New Unsupervised Multilingual Acoustic Laughter Segmentation Method
Sofia Callejas, Nahuel Gomez, Catherine Pelachaud +2
Laughter is a social non-vocalization that is universal across cultures and languages, and is crucial for human communication, including social bonding and communication signaling.…
Leveraging Wikidata for Geographically Informed Sociocultural Bias Dataset Creation: Application to Latin America
Yannis Karmim, Renato Pino, Hernan Contreras +6
Large Language Models (LLMs) exhibit inequalities with respect to various cultural contexts. Most prominent open-weights models are trained on Global North data and show prejudicia…
A Simple Method to Enhance Pre-trained Language Models with Speech Tokens for Classification
Nicolas Calbucura, Jose Guillen, Valentin Barriere
This paper presents a simple method that allows to easily enhance textual pre-trained large language models with speech information, when fine-tuned for a specific classification t…
StandUp4AI: A New Multilingual Dataset for Humor Detection in Stand-up Comedy Videos
Valentin Barriere, Nahuel Gomez, Leo Hemamou +2
Aiming towards improving current computational models of humor detection, we propose a new multimodal dataset of stand-up comedies, in seven languages: English, French, Spanish, It…
Fantastic Biases (What are They) and Where to Find Them
Valentin Barriere
Deep Learning models tend to learn correlations of patterns on huge datasets. The bigger these systems are, the more complex are the phenomena they can detect, and the more data th…
A Study of Nationality Bias in Names and Perplexity using Off-the-Shelf Affect-related Tweet Classifiers
Valentin Barriere, Sebastian Cifuentes
In this paper, we apply a method to quantify biases associated with named entities from various countries. We create counterfactual examples with small perturbations on target-doma…
Constructing a Real-World Benchmark for Early Wildfire Detection with the New PYRONEAR-2025 Dataset
Mateo Lostanlen, Nicolas Isla, Jose Guillen +4
Early wildfire detection (EWD) is of the utmost importance to enable rapid response efforts, and thus minimize the negative impacts of wildfire spreads. To this end, we present PYR…
Deep Natural Language Feature Learning for Interpretable Prediction
Felipe Urrutia, Cristian Buc, Valentin Barriere
We propose a general method to break down a main complex task into a set of intermediary easier sub-tasks, which are formulated in natural language as binary questions related to t…
Targeted Image Data Augmentation Increases Basic Skills Captioning Robustness
Valentin Barriere, Felipe del Rio, Andres Carvallo De Ferari +3
Artificial neural networks typically struggle in generalizing to out-of-context examples. One reason for this limitation is caused by having datasets that incorporate only partial…
Boosting Crop Classification by Hierarchically Fusing Satellite, Rotational, and Contextual Data
Valentin Barriere, Martin Claverie, Maja Schneider +2
Accurate in-season crop type classification is crucial for the crop production estimation and monitoring of agricultural parcels. However, the complexity of the plant growth patter…
Multimodal Crop Type Classification Fusing Multi-Spectral Satellite Time Series with Farmers Crop Rotations and Local Crop Distribution
Valentin Barriere, Martin Claverie
Accurate, detailed, and timely crop type mapping is a very valuable information for the institutions in order to create more accurate policies according to the needs of the citizen…
How does a Pre-Trained Transformer Integrate Contextual Keywords? Application to Humanitarian Computing
Barriere Valentin, Jacquet Guillaume
In a classification task, dealing with text snippets and metadata usually requires dealing with multimodal approaches. When those metadata are textual, it is tempting to use them i…
Improving Sentiment Analysis over non-English Tweets using Multilingual Transformers and Automatic Translation for Data-Augmentation
Valentin Barriere, Alexandra Balahur
Tweets are specific text data when compared to general text. Although sentiment analysis over tweets has become very popular in the last decade for English, it is still difficult t…
May I Check Again? -- A simple but efficient way to generate and use contextual dictionaries for Named Entity Recognition. Application to French Legal Texts
Valentin Barriere, Amaury Fouret
In this paper we present a new method to learn a model robust to typos for a Named Entity Recognition task. Our improvement over existing methods helps the model to take into accou…
Opinion Dynamics Modeling for Movie Review Transcripts Classification with Hidden Conditional Random Fields
Valentin Barriere, Chloé Clavel, Slim Essid
In this paper, the main goal is to detect a movie reviewer's opinion using hidden conditional random fields. This model allows us to capture the dynamics of the reviewer's opinion…