Étienne Simon


pdf bib
Fine-tuning and Sampling Strategies for Multimodal Role Labeling of Entities under Class Imbalance
Syrielle Montariol | Étienne Simon | Arij Riabi | Djamé Seddah
Proceedings of the Workshop on Combating Online Hostile Posts in Regional Languages during Emergency Situations

We propose our solution to the multimodal semantic role labeling task from the CONSTRAINT’22 workshop. The task aims at classifying entities in memes into classes such as “hero” and “villain”. We use several pre-trained multi-modal models to jointly encode the text and image of the memes, and implement three systems to classify the role of the entities. We propose dynamic sampling strategies to tackle the issue of class imbalance. Finally, we perform qualitative analysis on the representations of the entities.


pdf bib
Unsupervised Information Extraction: Regularizing Discriminative Approaches with Relation Distribution Losses
Étienne Simon | Vincent Guigue | Benjamin Piwowarski
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Unsupervised relation extraction aims at extracting relations between entities in text. Previous unsupervised approaches are either generative or discriminative. In a supervised setting, discriminative approaches, such as deep neural network classifiers, have demonstrated substantial improvement. However, these models are hard to train without supervision, and the currently proposed solutions are unstable. To overcome this limitation, we introduce a skewness loss which encourages the classifier to predict a relation with confidence given a sentence, and a distribution distance loss enforcing that all relations are predicted in average. These losses improve the performance of discriminative based models, and enable us to train deep neural networks satisfactorily, surpassing current state of the art on three different datasets.