Seq2Biseq - Bidirectional Output-wise Recurrent Neural Networks for Sequence Modelling
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Description
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.
The idea is similar to those used in Deliberation Networks, and Asynchronous bidirectional networks for Machine Translation.
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.
The idea is similar to those used in Deliberation Networks, and Asynchronous bidirectional networks for Machine Translation.
Features
- Bidirectional backward-forward decoding
Download
Please send me an email @univ-grenoble-alpes for now.
I'm going to git the tool.
I'm going to git the tool.
Licence
Seq2Biseq is provided under Creative-Commons BY-SA licence
Installation and usage
See the README file in the package.