Papers by Valentin Barriere
StandUp4AI: A New Multilingual Dataset for Humor Detection in Stand-up Comedy Videos (2025.findings-emnlp)
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| Challenge: | a new multimodal dataset of stand-up comedies is proposed to improve humor detection . the dataset is the biggest available for this type of task, and the most diverse . |
| Approach: | They propose a method to enhance the automatic laughter detection based on Audio Speech Recognition errors. |
| Outcome: | The proposed method improves existing models of humor detection by using audio speech recognition errors. |
Opinions in Interactions : New Annotations of the SEMAINE Database (2022.lrec-1)
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| Challenge: | a new method for the detection of opinions in interactions is proposed . a dataset of dyadic interactions is annotated continuously in two affective dimensions related to the emotions . |
| Approach: | They propose to annotate opinions over a multimodal corpus of dyadic interactions . they use a d-acting algorithm to annnotate the opinions of a speaker . |
| Outcome: | The proposed method allows to obtain a precise annotation regarding the opinion of a speaker. |
Deep Natural Language Feature Learning for Interpretable Prediction (2023.emnlp-main)
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| Challenge: | Using a small transformer language model, we can break down a complex task into a set of intermediary easier sub-tasks. |
| Approach: | They propose a method to break down a main task into a set of intermediary easier sub-tasks, which are formulated in natural language as binary questions related to the final target task. |
| Outcome: | The proposed method breaks down a complex task into a set of easier sub-tasks, which are formulated in natural language as binary questions related to the final target task. |
A Study of Nationality Bias in Names and Perplexity using Off-the-Shelf Affect-related Tweet Classifiers (2024.emnlp-main)
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| Challenge: | Recent research shows that named entities influence PLMs in many applications. |
| Approach: | They propose a method to quantify biases associated with named entities from various countries using Twitter data instead of templates or specific datasets. |
| Outcome: | The proposed method shows positive biases related to the language spoken in a country across all classifiers. |
Are Text Classifiers Xenophobic? A Country-Oriented Bias Detection Method with Least Confounding Variables (2024.lrec-main)
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| Challenge: | Existing methods for detecting biases are biased because of confounding variables . authors propose a method to detect the biased classifier on any type of unlabeled data . |
| Approach: | They propose a method to detect biases of a specific fine-tuned classifier on unlabeled data. |
| Outcome: | The proposed method detects biases on unlabeled data on named entity perturbations . it uses name-entity recognition on target-domain data and morphosynctactically different languages spoken in relation to countries of the target groups . |
Improving Sentiment Analysis over non-English Tweets using Multilingual Transformers and Automatic Translation for Data-Augmentation (2020.coling-main)
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| Challenge: | Existing models for sentiment analysis over tweets require a substantial amount of text to adapt to a domain where the syntax is different. |
| Approach: | They propose to use a multilingual transformer model to train over tweets in five different languages to adapt the model to non-English languages. |
| Outcome: | The proposed model improves over small corpora of tweets in non-English languages. |
Adapting Bias Evaluation to Domain Contexts using Generative Models (2025.emnlp-main)
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| Challenge: | Existing approaches to assess social bias in NLP systems face limitations in scalability and fidelity across domains. |
| Approach: | They propose a domain-adaptive framework that uses prompting with Large Language Models to automatically transform template-based bias datasets into domain-specific variants. |
| Outcome: | The proposed framework improves the accuracy and contextual relevance of bias evaluations in socially relevant datasets. |
CoFE: A New Dataset of Intra-Multilingual Multi-target Stance Classification from an Online European Participatory Democracy Platform (2022.aacl-short)
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| Challenge: | Stance Recognition is a useful tool for many real-life applications, from misinformation detection to poll verification. |
| Approach: | They propose to use an online debating platform where users can submit proposals and comment over proposals or over other comments. |
| Outcome: | The proposed dataset contains 4.2k proposals and 20k comments on various topics. |
The Touché23-ValueEval Dataset for Identifying Human Values behind Arguments (2024.lrec-main)
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Nailia Mirzakhmedova, Johannes Kiesel, Milad Alshomary, Maximilian Heinrich, Nicolas Handke, Xiaoni Cai, Valentin Barriere, Doratossadat Dastgheib, Omid Ghahroodi, MohammadAli SadraeiJavaheri, Ehsaneddin Asgari, Lea Kawaletz, Henning Wachsmuth, Benno Stein
| Challenge: | Cultural norms can influence the prioritization of values, leading to distinct perspectives on debatable topics. |
| Approach: | They present a Touché23-ValueEval dataset that annotates 4780 new arguments and annotated 54 human values. |
| Outcome: | The Touché23-ValueEval dataset doubles the original Webis-ArgValués-22 dataset to 9324 arguments. |