5 citations · 7 across the 4 of their papers we have counts for
8 papers
Multi-Task Sequence Prediction For Tunisian Arabizi Multi-Level Annotation
Elisa Gugliotta, Marco Dinarelli, Olivier Kraif
In this paper we propose a multi-task sequence prediction system, based on recurrent neural networks and used to annotate on multiple levels an Arabizi Tunisian corpus. The annotat…
TArC: Incrementally and Semi-Automatically Collecting a Tunisian Arabish Corpus
Elisa Gugliotta, Marco Dinarelli
This article describes the constitution process of the first morpho-syntactically annotated Tunisian Arabish Corpus (TArC). Arabish, also known as Arabizi, is a spontaneous coding…
A Data Efficient End-To-End Spoken Language Understanding Architecture
Marco Dinarelli, Nikita Kapoor, Bassam Jabaian +1
End-to-end architectures have been recently proposed for spoken language understanding (SLU) and semantic parsing. Based on a large amount of data, those models learn jointly acous…
Hybrid Neural Models For Sequence Modelling: The Best Of Three Worlds
Marco Dinarelli, Loïc Grobol
We propose a neural architecture with the main characteristics of the most successful neural models of the last years: bidirectional RNNs, encoder-decoder, and the Transformer mode…
Seq2Biseq: Bidirectional Output-wise Recurrent Neural Networks for Sequence Modelling
Marco Dinarelli, Loïc Grobol
During the last couple of years, Recurrent Neural Networks (RNN) have reached state-of-the-art performances on most of the sequence modelling problems. In particular, the "sequence…
Effective Spoken Language Labeling with Deep Recurrent Neural Networks
Marco Dinarelli, Yoann Dupont, Isabelle Tellier
Understanding spoken language is a highly complex problem, which can be decomposed into several simpler tasks. In this paper, we focus on Spoken Language Understanding (SLU), the m…