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During the last couple of years, Recurrent Neural Networks (RNN) have reachedstate-of-the-art performances on most of the sequence modelling problems. Inparticular, the "sequence to sequence" model and the neural CRF have proved tobe very effective in this domain. In this article, we propose a new RNNarchitecture for sequence labelling, leveraging gated recurrent layers to takearbitrarily long contexts into account, and using two decoders operatingforward and backward. We compare several variants of the proposed solution andtheir performances to the state-of-the-art. Most of our results are better thanthe state-of-the-art or very close to it and thanks to the use of recenttechnologies, our architecture can scale on corpora larger than those used inthis work.

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