no code implementations • JEPTALNRECITAL 2020 • Elham Mohammadi, Louis Marceau, Eric Charton, Leila Kosseim, Luka Nerima, Marie-Jean Meurs
Nous pr{\'e}sentons un mod{\`e}le d{'}apprentissage automatique qui combine mod{\`e}les neuronaux et linguistiques pour traiter les t{\^a}ches de classification dans lesquelles la distribution des {\'e}tiquettes des instances est d{\'e}s{\'e}quilibr{\'e}e. Les performances de ce mod{\`e}le sont mesur{\'e}es {\`a} l{'}aide d{'}exp{\'e}riences men{\'e}es sur les t{\^a}ches de classification de recettes de cuisine de la campagne DEFT 2013 (Grouin et al., 2013).
no code implementations • LREC 2020 • Elham Mohammadi, Timothe Beiko, Leila Kosseim
We experimented with a variety of corruption strategies to create synthetic incoherent pairs of discourse arguments from coherent ones.
no code implementations • LREC 2020 • Elham Mohammadi, Nada Naji, Louis Marceau, Marc Queudot, Eric Charton, Leila Kosseim, Marie-Jean Meurs
In this paper, we propose a neural-based model to address the first task of the DEFT 2013 shared task, with the main challenge of a highly imbalanced dataset, using state-of-the-art embedding approaches and deep architectures.
no code implementations • RANLP 2019 • Elham Mohammadi, Hessam Amini, Leila Kosseim
This paper describes a new approach for the task of contextual emotion detection.
Ranked #9 on Emotion Recognition in Conversation on EC
no code implementations • SEMEVAL 2019 • Elham Mohammadi, Hessam Amini, Leila Kosseim
This paper describes our system at SemEval 2019, Task 3 (EmoContext), which focused on the contextual detection of emotions in a dataset of 3-round dialogues.
no code implementations • WS 2019 • Elham Mohammadi, Hessam Amini, Leila Kosseim
This paper summarizes our participation to the CLPsych 2019 shared task, under the name CLaC.
no code implementations • WS 2017 • Elham Mohammadi, Hadi Veisi, Hessam Amini
Native language identification (NLI) is the task of determining an author{'}s native language, based on a piece of his/her writing in a second language.