Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Seq2Biseq: Bidirectional Output-wise Recurrent Neural Networks for
  Sequence Modelling   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 to sequence" model and the neural CRF have proved to
be very effective in this domain. In this article, we propose a new RNN
architecture for sequence labelling, leveraging gated recurrent layers to take
arbitrarily long contexts into account, and using two decoders operating
forward and backward. We compare several variants of the proposed solution and
their performances to the state-of-the-art. Most of our results are better than
the state-of-the-art or very close to it and thanks to the use of recent
technologies, our architecture can scale on corpora larger than those used in
this work.

16
The categories of this paper are: stat
33
The categories of this paper are: plasm-ph
29
The categories of this paper are: funct-an
11
The categories of this paper are: hep-lat
5
The categories of this paper are: gr-qc
13
The categories of this paper are: quant-ph
15
The categories of this paper are: nucl-ex
4
The categories of this paper are: cond-mat
3
The categories of this paper are: physics
27
The categories of this paper are: comp-gas
34
The categories of this paper are: chem-ph
36
The categories of this paper are: cmp-lg
18
The categories of this paper are: econ
35
The categories of this paper are: mtrl-th
8
The categories of this paper are: hep-ex
31
The categories of this paper are: ao-sci