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Section: New Results
Syntax modelling and treebank development
Participants : Djamé Seddah, Benoît Sagot, Éric Villemonte de La Clergerie, Emilia Verzeni, Wigdan Abbas Mekki Medeni, Elias Benaïssa, Farah Essaidi, Amal Fethi.
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In 2018, members of ALMAnaCH have finalised a conversion of the biggest annotated data set for French, the French Treebank, to Universal Dependencies 2.3, the now de facto standard for syntactic annotations [27]. The same group was also deeply involved in a proposal co-written with others leaders of the field [25], aiming at representing morpho-syntactic ambiguities from user-generated content and morphologically-rich languages. This proposal was implemented via the development of language specific analysers and data-driven normalised lexica [26].
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As part of the ANR Parsiti project, the development of gold standards for North-African dialectal Arabic has seen great progresses and is coming to a pre-release date in the first semester of 2019. This work involved more than 24 man.months over the last 12 months and will culminate with a multi-layered corpus of about 2000 sentences that is made of user-generated content with a highly variable dialect that contains up to 36% of French words and mixed syntax with Arabic. In order to assess the quality of the translation produced by the Parsiti project, we also included a translation layer (North-African Arabic-French) as well as all expected morpho-syntactic and syntactic annotations, following the state-of-the-art in terms of annotations. Papers are currently being written and will target the main NLP conferences of early 2019.
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In parallel to the last item, we also translated to English half of the French Social Media Bank which was developed in our previous project [92]. A morpho-syntactic annotation layer was added. The crucial difficulty was to maintain a symmetry in term of style and level of languages between French user-generated content and its English counterpart. This data set is currently being used in the Parsiti project in order to evaluate the MT models currently being developed by the LIMSI partner.