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 model. Evaluation on three sequence labelling tasks yields results that are close to the state-of-the-art for all tasks and better than it for some of them, showing the pertinence of this hybrid architecture for this kind of tasks.
@article{arxiv.1909.07102,
title = {Hybrid Neural Models For Sequence Modelling: The Best Of Three Worlds},
author = {Marco Dinarelli and Loïc Grobol},
journal= {arXiv preprint arXiv:1909.07102},
year = {2019}
}
Comments
12 pages + bibliography. English version, and slight improvement, of the paper published at the French conference TALN 2019