Marco Dinarelli with his first journal publication in a IEEE review    Marco Dinarelli
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LIG (UMR 5217)
Office 327
700 avenue Centrale
Campus de Saint-Martin-d’Hères, France

marco [dot] dinarelli [at] univ-grenoble-alpes [dot] fr
marco [dot] dinarelli [at] ens [dot] fr
marco [dot] dinarelli [at] gmail [dot] com

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Latest news

2019 / 02 / 22:
Paper accepted at CICling 2019 conference (International Conference on Intelligent Text Processing and Computational Linguistics)

2018 / 11 / 08:
Reading group on coreference resolution at LIG

Seq2Biseq - Bidirectional Output-wise Recurrent Neural Networks for Sequence Modelling

Content index:


Seq2Biseq tool is the software used for the paper Seq2Biseq: Bidirectional Output-wise Recurrent Neural Networks for Sequence Modelling. It replaces, extends and improves the previous tool LD-RNN, used for the paper Label-Dependencies Aware Recurrent Neural Networks.
Seq2Biseq is coded in pytorch and it follows the same research trend as our previous papers, where a bidirectional output-side context is used for current decision. A schema of the high-level architecture is shown in the following image.
Seq2Biseq model architecture

The idea is similar to those used in Deliberation Networks, and Asynchronous bidirectional networks for Machine Translation.


  • Bidirectional backward-forward decoding


Please send me an email @univ-grenoble-alpes for now.

I'm going to git the tool.


Seq2Biseq is provided under Creative-Commons BY-SA licence

Installation and usage

See the README file in the package.