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Found 19 papers, 4 papers with code
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.
Ré-ordonnancement via programmation dynamique pour l’adaptation cross-lingue d’un analyseur en dépendances (Sentence reordering via dynamic programming for cross-lingual dependency parsing )
Cet article s’intéresse au transfert cross-lingue d’analyseurs en dépendances et étudie des méthodes pour limiter l’effet potentiellement néfaste pour le transfert de divergences entre l’ordre des mots dans les langues source et cible.
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.
Span-based discontinuous constituency parsing: a family of exact chart-based algorithms with time complexities from O(n\^6) down to O(n\^3)
We introduce a novel chart-based algorithm for span-based parsing of discontinuous constituency trees of block degree two, including ill-nested structures.
Sparse Logistic Regression with High-order Features for Automatic Grammar Rule Extraction from Treebanks
More specifically, we extract descriptions and rules across different languages for two linguistic phenomena, agreement and word order, using a large search space and paying special attention to the ranking order of the extracted rules.
SaulLM-7B: A pioneering Large Language Model for Law
In this paper, we introduce SaulLM-7B, a large language model (LLM) tailored for the legal domain.
CroissantLLM: A Truly Bilingual French-English Language Model
We introduce CroissantLLM, a 1. 3B language model pretrained on a set of 3T English and French tokens, to bring to the research and industrial community a high-performance, fully open-sourced bilingual model that runs swiftly on consumer-grade local hardware.
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.
On the inconsistency of separable losses for structured prediction
In this paper, we prove that separable negative log-likelihood losses for structured prediction are not necessarily Bayes consistent, or, in other words, minimizing these losses may not result in a model that predicts the most probable structure in the data distribution for a given input.
A dynamic programming algorithm for span-based nested named-entity recognition in O(n^2)
Span-based nested named-entity recognition (NER) has a cubic-time complexity using a variant of the CYK algorithm.
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.
Sur l'impact des contraintes structurelles pour l'analyse en d\'ependances profondes fond\'ee sur les graphes (On the impact of structural constraints for graph-based deep dependency parsing)
Les algorithmes existants pour l{'}analyse en d{\'e}pendances profondes fond{\'e}e sur les graphes capables de garantir la connexit{\'e} des structures produites ne couvrent pas les corpus du fran{\c{c}}ais.
Span-based discontinuous constituency parsing: a family of exact chart-based algorithms with time complexities from O(n^6) down to O(n^3)
We introduce a novel chart-based algorithm for span-based parsing of discontinuous constituency trees of block degree two, including ill-nested structures.
Learning Latent Trees with Stochastic Perturbations and Differentiable Dynamic Programming
We treat projective dependency trees as latent variables in our probabilistic model and induce them in such a way as to be beneficial for a downstream task, without relying on any direct tree supervision.
Differentiable Perturb-and-Parse: Semi-Supervised Parsing with a Structured Variational Autoencoder
Human annotation for syntactic parsing is expensive, and large resources are available only for a fraction of languages.
Efficient Discontinuous Phrase-Structure Parsing via the Generalized Maximum Spanning Arborescence
We present a new method for the joint task of tagging and non-projective dependency parsing.
