Search Results for author:
Found 5 papers, 0 papers with code
Un algorithme d’analyse sémantique fondée sur les graphes via le problème de l’arborescence généralisée couvrante (A graph-based semantic parsing algorithm via the generalized spanning arborescence problem)
Nous proposons un nouvel algorithme pour l’analyse sémantique fondée sur les graphes via le problème de l’arborescence généralisée couvrante.
Auto-encodeurs variationnels : contrecarrer le problème de posterior collapse grâce à la régularisation du décodeur (Variational auto-encoders : prevent posterior collapse via decoder regularization)
Les auto-encodeurs variationnels sont des modèles génératifs utiles pour apprendre des représentations latentes.
Structural generalization in COGS: Supertagging is (almost) all you need
In many Natural Language Processing applications, neural networks have been found to fail to generalize on out-of-distribution examples.
On graph-based reentrancy-free semantic parsing
We propose a novel graph-based approach for semantic parsing that resolves two problems observed in the literature: (1) seq2seq models fail on compositional generalization tasks; (2) previous work using phrase structure parsers cannot cover all the semantic parses observed in treebanks.
Preventing posterior collapse in variational autoencoders for text generation via decoder regularization
Variational autoencoders trained to minimize the reconstruction error are sensitive to the posterior collapse problem, that is the proposal posterior distribution is always equal to the prior.
