Search Results for author: Chlo{\'e} Braud

Found 15 papers, 2 papers with code

Investigation par m\'ethodes d'apprentissage des sp\'ecificit\'es langagi\`eres propres aux personnes avec schizophr\'enie (Investigating Learning Methods Applied to Language Specificity of Persons with Schizophrenia)

no code implementations JEPTALNRECITAL 2020 Maxime Amblard, Chlo{\'e} Braud, Chuyuan Li, Caroline Demily, Nicolas Franck, Michel Musiol

Nous pr{\'e}sentons des exp{\'e}riences visant {\`a} identifier automatiquement des patients pr{\'e}sentant des sympt{\^o}mes de schizophr{\'e}nie dans des conversations contr{\^o}l{\'e}es entre patients et psychoth{\'e}rapeutes.


Which aspects of discourse relations are hard to learn? Primitive decomposition for discourse relation classification

no code implementations WS 2019 Charlotte Roze, Chlo{\'e} Braud, Philippe Muller

Discourse relation classification has proven to be a hard task, with rather low performance on several corpora that notably differ on the relation set they use.

General Classification Relation Classification

Aligning Discourse and Argumentation Structures using Subtrees and Redescription Mining

no code implementations WS 2019 Laurine Huber, Yannick Toussaint, Charlotte Roze, Mathilde Dargnat, Chlo{\'e} Braud

In this paper, we investigate similarities between discourse and argumentation structures by aligning subtrees in a corpus containing both annotations.

EusDisParser: improving an under-resourced discourse parser with cross-lingual data

no code implementations WS 2019 Mikel Iruskieta, Chlo{\'e} Braud

More precisely, we build a monolingual system using the small set of data available and investigate the use of multilingual word embeddings to train a system for Basque using data annotated for another language.

Multilingual Word Embeddings

When does deep multi-task learning work for loosely related document classification tasks?

no code implementations WS 2018 Emma Kerinec, Chlo{\'e} Braud, Anders S{\o}gaard

This work aims to contribute to our understanding of \textit{when} multi-task learning through parameter sharing in deep neural networks leads to improvements over single-task learning.

Document Classification General Classification +5

Is writing style predictive of scientific fraud?

no code implementations WS 2017 Chlo{\'e} Braud, Anders S{\o}gaard

The problem of detecting scientific fraud using machine learning was recently introduced, with initial, positive results from a model taking into account various general indicators.

Logical Reasoning

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