Christophe Servan
Human & AI together

Research

My main interest is in Deep Learning for multilingual Natural Language Processing applied to Dialogue Systems and Information Retrieval.

I focus on responsible models for several fields of NLP: Spoken Language Understanding, semantic content analysis and modeling, machine translation, Intent Detection in real-life application at scale.

Teaching

Since years I'm giving NLP courses to master or engineer students for instance:

... and many other computer science courses such like programming, databases, etc.

Management

I am Researcher at the LISN-CNRS lab and part-time Research Manager at Qwant, leading the NLP topic at QWANT.

Member of the board of French scientific associations:

  • President of ATALA (French NLP association)
  • Deputy-treasurer of ARIA (French IR association)

Biography

NLP Researcher at the LISN-CNRS lab since 2022 and part-time Research Scientist and Manager at QWANT since 2018, he was Deputy director of the INRIA-QWANT Lab from 2018 to 2021.

Before joining LISN-CNRS lab and Qwant, he was applied researcher at SYSTRAN to work on domain adaptation for NMT (2016-2017), member of the GETALP team at the University of Grenoble to develop Deep Learning approaches for SMT evaluation (2015-2016), part of the Xerox Research Centre Europe (XRCE) in the Machine Learning for Document Access and Translation (MLDAT) group (2013-2015), postdoc researcher in the Speech and Language Technology group at the laboratory of the University of Le Mans, France (LIUM), research engineer at the Vision & Content Engineering Laboratory at the CEA LIST (2009-2010).

He received a PhD in Computer Science from the University of Avignon, France in 2008.

Since February 2022, I'm part of the LISN-CNRS lab

Latest news

    Since February 2022, I'm part of the LISN-CNRS lab.

    • the French version of ALBERT model (FrALBERT) is available on HuggingFace, check this out:
      https://huggingface.co/qwant/fralbert-base

    • Oralie cattan, Christophe Servan and Sophie Rosset. On the Usability of Transformers-based models for a French Question-Answering task. In Proceedings of the Joint Conference of the Information Retrieval Communities in Europe (CIRCLE) 2022, Samatan, France, July 2022. [PDF]
    • Oralie cattan, Christophe Servan and Sophie Rosset. Benchmarking Transformers-based models on French Spoken Language Understanding tasks. In Proceedings of INTERSPEECH 2022, Incheon, South Korea, September 2022. [PDF]

Contact

QWANT: https://www.qwant.com

Address:
    QWANT
    10 Boulevard Haussmann
    75009 paris, France.

Phone: +33 (0)1 83 64 89 37

Email: c dot servan at qwant dot com

Last updated on 2023-02-22