Section: Partnerships and Cooperations

European Initiatives

FP7 & H2020 Projects

Participants : Aurelien Bellet, Marc Tommasi, Brij Mohan Lal Srivastava.

  • Program: H2020 ICT-29-2018 (RIA)

  • Project acronym: COMPRISE

  • Project title: Cost-effective, Multilingual, Privacy-driven voice-enabled Services

  • Duration: Dec 2018 - Nov 2021

  • Coordinator: Emmanuel Vincent [Inria Nancy - Grand Est]

  • Other partners: Inria Multispeech, Ascora GmbH, Netfective Technology SA, Rooter Analysis SL, Tilde SIA, University of Saarland

  • Abstract: COMPRISE will define a fully private-by-design methodology and tools that will reduce the cost and increase the inclusiveness of voice interaction technologies.

Collaborations in European Programs, Except FP7 & H2020

  • Program: Bilateral ANR project with Luxembourg

  • Project acronym: SLANT

  • Project title: Spin and Bias in Language Analyzed in News and Texts

  • Duration: Dec 2019 - June 2023

  • Coordinator: Philippe Muller [Université Paul Sabatier]

  • Other partners: IRIT (Toulouse), SnT (Luxembourg)

  • Abstract: There is a growing concern about misinformation or biased information in public communication, whether in traditional media or social forums. While automating fact-checking has received a lot of attention, the problem of fair information is much larger and includes more insidious forms like biased presentation of events and discussion. The SLANT project aims at characterizing bias in textual data, either intended, in public reporting, or unintended in writing aiming at neutrality. An abstract model of biased interpretation using work on discourse structure, semantics and interpretation will be complemented and concretized by finding relevant lexical, syntactic, stylistic or rhetorical differences through an automated but explainable comparison of texts with different biases on the same subject, based on a dataset of news media coverage from a diverse set of sources. We will also explore how our results can help alter bias in texts or remove it from automated representations of texts.