Über mich

Ich bin ein Doktorand der Informatik an der Sorbonne Universität und an der Almanach Forscherteam an Inria.

Ich arbeite hauptsächlich in den Bereichen maschinelles Lernen, Deep Learning und NLP.

Ich liebe Kaffee, Kekse und Codierung.

Interessen
  • Machine Learning
  • Deep Learning
  • Text Mining
  • NLP
Bildung

  • Ph.D. in Informatik

    Sorbonne Université

  • BASc MIASHS, 2018

    Université Paris 8

  • MSc in Mathematik, 2017

    Aix-Marseille Université

  • BSc in Mathematik, 2016

    Universidad Nacional de Colombia

Ausgewählte Veröffentlichungen

Quality at a Glance: An Audit of Web-Crawled Multilingual Datasets
We audit 5 multilingual corpora, finding that lower-resource corpora have systematic issues.
Isaac Caswell, Julia Kreutzer, Lisa Wang, Ahsan Wahab, Daan van Esch, Nasanbayar Ulzii-Orshikh, Allahsera Tapo, Nishant Subramani, Artem Sokolov, Claytone Sikasote, Monang Setyawan, Supheakmungkol Sarin, Sokhar Samb, Benoît Sagot, Clara Rivera, Annette Rios, Isabel Papadimitriou, Salomey Osei, Pedro Javier Ortiz Suárez, Iroro Orife, Kelechi Ogueji, Rubungo Andre Niyongabo, Toan Q. Nguyen, Mathias Müller, André Müller, Shamsuddeen Hassan Muhammad, Nanda Muhammad, Ayanda Mnyakeni, Jamshidbek Mirzakhalov, Tapiwanashe Matangira, Colin Leong, Nze Lawson, Sneha Kudugunta, Yacine Jernite, Mathias Jenny, Orhan Firat, Bonaventure F. P. Dossou, Sakhile Dlamini, Nisansa de Silva, Sakine Çabuk Ballı, Stella Biderman, Alessia Battisti, Ahmed Baruwa, Ankur Bapna, Pallavi Baljekar, Israel Abebe Azime, Ayodele Awokoya, Duygu Ataman, Orevaoghene Ahia, Oghenefego Ahia, Sweta Agrawal, Mofetoluwa Adeyemi
PDF Zitieren HAL arXiv
A Monolingual Approach to Contextualized Word Embeddings for Mid-Resource Languages
We explore the impact of the training corpus on contextualized word embeddings in five mid-resource languages.
PDF Zitieren Datensatz Projekt Video DOI ACL Anthology ACL 2020 HAL arXiv
A Monolingual Approach to Contextualized Word Embeddings for Mid-Resource Languages
CamemBERT: a Tasty French Language Model
We explore the impact of the training data size on a French version of RoBERTa. (Equal contribution by the first three authors).
PDF Zitieren Datensatz Projekt Video DOI ACL Anthology arXiv Website ACL 2020 HAL
CamemBERT: a Tasty French Language Model
Asynchronous Pipeline for Processing Huge Corpora on Medium to Low Resource Infrastructures
We propose a new pipeline to filter, clean and classify Common Crawl by language, we publish the final corpus under the name OSCAR.
PDF Zitieren Code Datensatz Projekt Folien DOI CMLC-7 Website HAL
Asynchronous Pipeline for Processing Huge Corpora on Medium to Low Resource Infrastructures

Aktuelle Veröffentlichungen

Quality at a Glance: An Audit of Web-Crawled Multilingual Datasets
We audit 5 multilingual corpora, finding that lower-resource corpora have systematic issues.
Isaac Caswell, Julia Kreutzer, Lisa Wang, Ahsan Wahab, Daan van Esch, Nasanbayar Ulzii-Orshikh, Allahsera Tapo, Nishant Subramani, Artem Sokolov, Claytone Sikasote, Monang Setyawan, Supheakmungkol Sarin, Sokhar Samb, Benoît Sagot, Clara Rivera, Annette Rios, Isabel Papadimitriou, Salomey Osei, Pedro Javier Ortiz Suárez, Iroro Orife, Kelechi Ogueji, Rubungo Andre Niyongabo, Toan Q. Nguyen, Mathias Müller, André Müller, Shamsuddeen Hassan Muhammad, Nanda Muhammad, Ayanda Mnyakeni, Jamshidbek Mirzakhalov, Tapiwanashe Matangira, Colin Leong, Nze Lawson, Sneha Kudugunta, Yacine Jernite, Mathias Jenny, Orhan Firat, Bonaventure F. P. Dossou, Sakhile Dlamini, Nisansa de Silva, Sakine Çabuk Ballı, Stella Biderman, Alessia Battisti, Ahmed Baruwa, Ankur Bapna, Pallavi Baljekar, Israel Abebe Azime, Ayodele Awokoya, Duygu Ataman, Orevaoghene Ahia, Oghenefego Ahia, Sweta Agrawal, Mofetoluwa Adeyemi
PDF Zitieren HAL arXiv
SinNer@CLEF-HIPE2020: Sinful Adaptation of SotA models for Named Entity Recognition in Historical French and German Newspapers
In this article we present the approaches developed by the Sorbonne-INRIA for NER (SinNer) team for the CLEF-HIPE 2020 challenge on Named Entity Processing on old newspapers.
PDF Zitieren Video CEUR-WS CLEF-HIPE-2020 CLEF-2020 HAL
A Monolingual Approach to Contextualized Word Embeddings for Mid-Resource Languages
We explore the impact of the training corpus on contextualized word embeddings in five mid-resource languages.
PDF Zitieren Datensatz Projekt Video DOI ACL Anthology ACL 2020 HAL arXiv
A Monolingual Approach to Contextualized Word Embeddings for Mid-Resource Languages
CamemBERT: a Tasty French Language Model
We explore the impact of the training data size on a French version of RoBERTa. (Equal contribution by the first three authors).
PDF Zitieren Datensatz Projekt Video DOI ACL Anthology arXiv Website ACL 2020 HAL
CamemBERT: a Tasty French Language Model
Building a User-Generated Content North-African Arabizi Treebank: Tackling Hell
We introduce the first treebank for a romanized user-generated content variety of Algerian, a North-African Arabic dialect.
PDF Zitieren Video DOI ACL Anthology ACL 2020
Les modèles de langue contextuels Camembert pour le Français : impact de la taille et de l'hétérogénéité des données d'entrainement
We explore the impact of the training data size and heterogeneity on French language modeling. (Equal contribution by the first three authors).
PDF Zitieren Datensatz Projekt TALN 2020 HAL Website
Les modèles de langue contextuels Camembert pour le Français : impact de la taille et de l'hétérogénéité des données d'entrainement
Establishing a New State-of-the-Art for French Named Entity Recognition
We explore convert the NER annotations of the French TreeBank to a more user-friendly format and establish a new state of the art for French NER.
PDF Zitieren LREC 2020 HAL arXiv ACL Anthology
Establishing a New State-of-the-Art for French Named Entity Recognition
French Contextualized Word-Embeddings with a sip of CaBeRnet: a New French Balanced Reference Corpus
We investigate the impact of different types and size of training corpora on language models.
PDF Zitieren CMLC-8 ACL Anthology HAL
French Contextualized Word-Embeddings with a sip of CaBeRnet: a New French Balanced Reference Corpus
How OCR Performance can Impact on the Automatic Extraction of Dictionary Content Structures
We explore the impact of the OCR quality on grobid-dictionaries models.
PDF Zitieren Projekt TEI 2019 HAL
Asynchronous Pipeline for Processing Huge Corpora on Medium to Low Resource Infrastructures
We propose a new pipeline to filter, clean and classify Common Crawl by language, we publish the final corpus under the name OSCAR.
PDF Zitieren Code Datensatz Projekt Folien DOI CMLC-7 Website HAL
Asynchronous Pipeline for Processing Huge Corpora on Medium to Low Resource Infrastructures

Aktuelle und kommende Vorträge

Des Méthodes de TAL modernes pour l'Enrichissement de Documents
Nous présentons une pipeline pour le traitement et l’enrichissement de documents basée sur les dernières méthodes d’apprentissage neuronal.
Sep 22, 2020
Folien Follow
A Monolingual Approach to Contextualized Word Embeddings for Mid-Resource Languages
We explore the impact of the training corpus on contextualized word embeddings in five mid-resource languages.
Jul 6, 2020
Folien Video Follow
A Monolingual Approach to Contextualized Word Embeddings for Mid-Resource Languages
Asynchronous Pipeline for Processing Huge Corpora on Medium to Low Resource Infrastructures
We propose a new pipeline to filter, clean and classify Common Crawl by language, we publish the final corpus under the name OSCAR.
Jul 22, 2019
PDF Code Folien Follow
Preparing the Dictionnaire Universel for Automatic Enrichment
A talk about automatic enrichment of dictionaries.
Jun 13, 2019
Folien Follow
Reducing computation time by months by rewriting Bash scripts in Go
Mar 24, 2019
Code Folien Follow

Projekte

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All Language Modeling Corpus Linguistics Digital Humanities

BASNUM

Digitization and analysis of Basnage de Beauval’s Universal Dictionary: lexicography and scientific networks

CamemBERT

CamemBERT

A state-of-the-art language model for French.

OSCAR

OSCAR

OSCAR or Open Super-large Crawled ALMAnaCH coRpus is a huge multilingual corpus.

Kontakt