8 citations · 15 across the 10 of their papers we have counts for
3 papers · 1 filter
Towards Unsupervised Content Disentanglement in Sentence Representations via Syntactic Roles
Ghazi Felhi, Joseph Le Roux, Djamé Seddah
Linking neural representations to linguistic factors is crucial in order to build and analyze NLP models interpretable by humans. Among these factors, syntactic roles (e.g. subject…
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…