Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Cell-aware Stacked LSTMs for Modeling Sentences   We propose a method of stacking multiple long short-term memory (LSTM) layers
for modeling sentences. In contrast to the conventional stacked LSTMs where
only hidden states are fed as input to the next layer, the suggested
architecture accepts both hidden and memory cell states of the preceding layer
and fuses information from the left and the lower context using the soft gating
mechanism of LSTMs. Thus the architecture modulates the amount of information
to be delivered not only in horizontal recurrence but also in vertical
connections, from which useful features extracted from lower layers are
effectively conveyed to upper layers. We dub this architecture Cell-aware
Stacked LSTM (CAS-LSTM) and show from experiments that our models bring
significant performance gain over the standard LSTMs on benchmark datasets for
natural language inference, paraphrase detection, sentiment classification, and
machine translation. We also conduct extensive qualitative analysis to
understand the internal behavior of the suggested approach.

30
The categories of this paper are: q-alg
29
The categories of this paper are: funct-an
34
The categories of this paper are: chem-ph
13
The categories of this paper are: quant-ph
19
The categories of this paper are: eess
0
The categories of this paper are: hep-ph
9
The categories of this paper are: nlin
22
The categories of this paper are: chao-dyn
28
The categories of this paper are: alg-geom
10
The categories of this paper are: q-bio
16
The categories of this paper are: stat
14
The categories of this paper are: math-ph
25
The categories of this paper are: patt-sol
32
The categories of this paper are: atom-ph
36
The categories of this paper are: cmp-lg
21
The categories of this paper are: adap-org