Postdoctoral position in Natural Language Processing: Text Simplification

Project description

The goal of the ALECTOR project is to develop and test resources that make it possible to propose simplified texts for children who face major problems in reading and understanding written texts.

Job description

The successful candidate will develop methods for text simplification, which will be applied to French, with a focus on children with dyslexia.

The work will be built on existing simplification systems, which will be used for comparison, such as LEXenstein, which is specific for lexical simplification, or the methods described in (Zhang and Lapata, 2017), (Nisioi et al., 2017) or (Stajner et al., 2017).

Evaluation will be both automatic, by using MT metrics on existing corpora, and manual, in order to focus on specific issues.

Tests will be made with existing English datasets and tools, but will also be applied to French.

The appointed researcher will work in close collaboration with all teams involved in the ALECTOR project.

Requirements and qualifications

  • Ph.D. in Computer Science with a focus on Machine Learning or Natural Language Processing
  • Experience in text simplification, machine translation or summarization would be an asset
  • Experience with deep learning tools
  • Good publication record
  • Ability to work in a collaborative environment, with a strong commitment to achieving research goals
  • Fluent French and/or English, strong oral and written communication skills
  • Solid programming skills

Additional information

  • Application deadline: open until filled
  • Starting date: September to December 2018
  • Duration: 1 year, renewable depending on performance

Applications

Applications should include the following:

  • Cover letter outlining interest in the position and academic goals
  • CV including a list of publications
  • Recommendation letters or names and contact information of at least two referees

and be sent to Anne-Laure Ligozat (annlor[@]limsi.fr)

LIMSI
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