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Le projet FREEM : ressources, outils et enjeux pour l’étude du français d’Ancien Régime

We present annotated corpora and NLP models for some downstream tasks in Early Modern French.

Simon Gabay, Pedro Ortiz Suarez, Rachel Bawden, Alexandre Bartz, Philippe Gambette, Benoît Sagot

Le projet FREEM : ressources, outils et enjeux pour l’étude du français d’Ancien Régime

BERTrade: Using Contextual Embeddings to Parse Old French

We consider several neural language models, some of which trained or fine-tuned on a new corpus of raw Old and Middle French texts, and use their internal representations of words as inputs to train taggers and parsers on the SRCMF treebank.

Loïc Grobol, Mathilde Regnault, Pedro Ortiz Suarez, Benoît Sagot, Laurent Romary, Benoît Crabbé

BERTrade: Using Contextual Embeddings to Parse Old French

Ungoliant: An Optimized Pipeline for the Generation of a Very Large-Scale Multilingual Web Corpus

We propose a new pipeline that is faster, modular, parameterizable, and well documented. We use it to create a corpus similar to OSCAR but larger and based on recent data.

Julien Abadji, Pedro Ortiz Suarez, Laurent Romary, Benoît Sagot

Ungoliant: An Optimized Pipeline for the Generation of a Very Large-Scale Multilingual Web Corpus

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.

Pedro Ortiz Suarez, Yoann Dupont, Gaël Lejeune, Tian Tian

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.

Pedro Ortiz Suarez, Laurent Romary, Benoît Sagot

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).

Louis Martin, Benjamin Muller, Pedro Ortiz Suarez, Yoann Dupont, Laurent Romary, Éric de la Clergerie, Djamé Seddah, Benoît Sagot

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.

Djamé Seddah, Farah Essaidi, Amal Fethi, Matthieu Futeral, Benjamin Muller, Pedro Ortiz Suarez, Benoît Sagot, Abhishek Srivastava

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).

Louis Martin, Benjamin Muller, Pedro Ortiz Suarez, Yoann Dupont, Laurent Romary, Éric de la Clergerie, Benoît Sagot, Djamé Seddah

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.

Pedro Ortiz Suarez, Yoann Dupont, Benjamin Muller, Laurent Romary, Benoît Sagot

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.

Murielle Popa-Fabre, Pedro Ortiz Suarez, Benoît Sagot, Éric de la Clergerie

French Contextualized Word-Embeddings with a sip of CaBeRnet: a New French Balanced Reference Corpus