no code implementations • EMNLP (insights) 2020 • Ashwin Geet D’Sa, Irina Illina, Dominique Fohr, Dietrich Klakow, Dana Ruiter
In this paper, label propagation-based semi-supervised learning is explored for the task of hate speech classification.
no code implementations • JEP/TALN/RECITAL 2022 • Nicolas Zampieri, Carlos Ramisch, Irina Illina, Dominique Fohr
L’identification des expressions polylexicales (EP) dans les tweets est une tâche difficile en raison de la nature linguistique complexe des EP combinée à l’utilisation d’un langage non standard.
no code implementations • NAACL (SocialNLP) 2021 • Tulika Bose, Irina Illina, Dominique Fohr
The state-of-the-art abusive language detection models report great in-corpus performance, but underperform when evaluated on abusive comments that differ from the training scenario.
no code implementations • NAACL (NLP4IF) 2021 • Tulika Bose, Irina Illina, Dominique Fohr
Rapidly changing social media content calls for robust and generalisable abuse detection models.
no code implementations • LREC 2022 • Nicolas Zampieri, Carlos Ramisch, Irina Illina, Dominique Fohr
In this article, we present joint experiments on these two related tasks on English Twitter data: first we focus on the MWE identification task, and then we observe the influence of MWE-based features on the HSD task.
no code implementations • 17 Oct 2022 • Tulika Bose, Irina Illina, Dominique Fohr
The concerning rise of hateful content on online platforms has increased the attention towards automatic hate speech detection, commonly formulated as a supervised classification task.
no code implementations • COLING 2022 • Tulika Bose, Nikolaos Aletras, Irina Illina, Dominique Fohr
State-of-the-art approaches for hate-speech detection usually exhibit poor performance in out-of-domain settings.
1 code implementation • LREC 2022 • Dana Ruiter, Liane Reiners, Ashwin Geet D'Sa, Thomas Kleinbauer, Dominique Fohr, Irina Illina, Dietrich Klakow, Christian Schemer, Angeliki Monnier
Even though hate speech (HS) online has been an important object of research in the last decade, most HS-related corpora over-simplify the phenomenon of hate by attempting to label user comments as "hate" or "neutral".
1 code implementation • Findings (ACL) 2022 • Tulika Bose, Nikolaos Aletras, Irina Illina, Dominique Fohr
In this paper, we propose to automatically identify and reduce spurious correlations using attribution methods with dynamic refinement of the list of terms that need to be regularized during training.
no code implementations • 1 Jun 2021 • Nicolas Zampieri, Irina Illina, Dominique Fohr
To incorporate MWE features, we create a three-branch deep neural network: one branch for USE, one for MWE categories, and one for MWE embeddings.
no code implementations • 2 Nov 2020 • Dominique Fohr, Irina Illina
We propose to perform this through rescoring of the ASR N-best hypotheses list.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +2
no code implementations • JEPTALNRECITAL 2020 • Mohamed Amine Menacer, Dominique Fohr, Denis Jouvet, Karima Abidi, David Langlois, Kamel Sma{\"\i}li
Un autre objectif du projet {\'e}tait aussi de comparer les opinions et sentiments exprim{\'e}s dans plusieurs vid{\'e}os comparables.
no code implementations • JEPTALNRECITAL 2020 • St{\'e}phane Level, Irina Illina, Dominique Fohr
Malgr{\'e} les avanc{\'e}s spectaculaires ces derni{\`e}res ann{\'e}es, les syst{\`e}mes de Reconnaissance Automatique de Parole (RAP) commettent encore des erreurs, surtout dans des environnements bruit{\'e}s. Pour am{\'e}liorer la RAP, nous proposons de se diriger vers une contextualisation d{'}un syst{\`e}me RAP, car les informations s{\'e}mantiques sont importantes pour la performance de la RAP.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +1
no code implementations • JEPTALNRECITAL 2020 • Ismael Bada, Dominique Fohr, Irina Illina
Pour prendre en compte ce probl{\`e}me de prononciations erron{\'e}es, notre approche propose d{'}int{\'e}grer les prononciations non natives dans le lexique et par la suite d{'}utiliser ce lexique enrichi pour la reconnaissance.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +1
no code implementations • LREC 2020 • Ashwin Geet D'Sa, Irina Illina, Dominique Fohr
The contribution of this paper is the design of binary classification and regression-based approaches aiming to predict whether a comment is toxic or not.
no code implementations • WS 2017 • Mohamed Amine Menacer, Odile Mella, Dominique Fohr, Denis Jouvet, David Langlois, Kamel Smaili
Despite all the classical techniques for Automatic Speech Recognition (ASR), which can be efficiently applied to Arabic speech recognition, it is essential to take into consideration the language specificities to improve the system performance.
no code implementations • COLING 2016 • Guillaume Serri{\`e}re, Christophe Cerisara, Dominique Fohr, Odile Mella
This work proposes a new confidence measure for evaluating text-to-speech alignment systems outputs, which is a key component for many applications, such as semi-automatic corpus anonymization, lips syncing, film dubbing, corpus preparation for speech synthesis and speech recognition acoustic models training.
no code implementations • LREC 2016 • Imran Sheikh, Irina Illina, Dominique Fohr
Out-Of-Vocabulary (OOV) words missed by Large Vocabulary Continuous Speech Recognition (LVCSR) systems can be recovered with the help of topic and semantic context of the OOV words captured from a diachronic text corpus.
no code implementations • LREC 2016 • Juergen Trouvain, Anne Bonneau, Vincent Colotte, Camille Fauth, Dominique Fohr, Denis Jouvet, Jeanin J{\"u}gler, Yves Laprie, Odile Mella, Bernd M{\"o}bius, Frank Zimmerer
The IFCASL corpus is a French-German bilingual phonetic learner corpus designed, recorded and annotated in a project on individualized feedback in computer-assisted spoken language learning.
no code implementations • 17 Nov 2015 • Imran Sheikh, Irina Illina, Dominique Fohr, Georges Linarès
In this paper, we propose two neural network models targeted to retrieve OOV PNs relevant to an audio document: (a) Document level Continuous Bag of Words (D-CBOW), (b) Document level Continuous Bag of Weighted Words (D-CBOW2).
no code implementations • LREC 2014 • Camille Fauth, Anne Bonneau, Frank Zimmerer, Juergen Trouvain, Bistra Andreeva, Vincent Colotte, Dominique Fohr, Denis Jouvet, Jeanin J{\"u}gler, Yves Laprie, Odile Mella, Bernd M{\"o}bius
We present the design of a corpus of native and non-native speech for the language pair French-German, with a special emphasis on phonetic and prosodic aspects.
no code implementations • JEPTALNRECITAL 2012 • Luiza Orosanu, Denis Jouvet, Dominique Fohr, Irina Illina, Anne Bonneau
no code implementations • LREC 2012 • Dominique Fohr, Odile Mella
In this paper, we propose a GPL software CoALT (Comparing Automatic Labelling Tools) for comparing two automatic labellers or two speech-text alignment tools, ranking them and displaying statistics about their differences.