6 citations · 6 across the 7 of their papers we have counts for
5 papers · 1 filter
Diffusion-MF: Approximate Structured Diffusion for Sequence Labelling
Nicolas Floquet, Joseph Le Roux, Nadi Tomeh
We introduce Diffusion-MF, a discrete diffu- sion sequence labeller that places a linear-chain conditional random field (LCRF) inside the denoising loop. Unlike prior diffusion lab…
Scaling Graph-Based Dependency Parsing with Arc Vectorization and Attention-Based Refinement
Nicolas Floquet, Joseph Le Roux, Nadi Tomeh +1
We propose a novel architecture for graph-based dependency parsing that explicitly constructs vectors, from which both arcs and labels are scored. Our method addresses key limitati…
Exploiting Inductive Bias in Transformers for Unsupervised Disentanglement of Syntax and Semantics with VAEs
Ghazi Felhi, Joseph Le Roux, Djamé Seddah
We propose a generative model for text generation, which exhibits disentangled latent representations of syntax and semantics. Contrary to previous work, this model does not need s…
AraBART: a Pretrained Arabic Sequence-to-Sequence Model for Abstractive Summarization
Moussa Kamal Eddine, Nadi Tomeh, Nizar Habash +2
Like most natural language understanding and generation tasks, state-of-the-art models for summarization are transformer-based sequence-to-sequence architectures that are pretraine…
Challenging the Semi-Supervised VAE Framework for Text Classification
Ghazi Felhi, Joseph Le Roux, Djamé Seddah
Semi-Supervised Variational Autoencoders (SSVAEs) are widely used models for data efficient learning. In this paper, we question the adequacy of the standard design of sequence SSV…