Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Linguistic Knowledge as Memory for Recurrent Neural Networks   Training recurrent neural networks to model long term dependencies is
difficult. Hence, we propose to use external linguistic knowledge as an
explicit signal to inform the model which memories it should utilize.
Specifically, external knowledge is used to augment a sequence with typed edges
between arbitrarily distant elements, and the resulting graph is decomposed
into directed acyclic subgraphs. We introduce a model that encodes such graphs
as explicit memory in recurrent neural networks, and use it to model
coreference relations in text. We apply our model to several text comprehension
tasks and achieve new state-of-the-art results on all considered benchmarks,
including CNN, bAbi, and LAMBADA. On the bAbi QA tasks, our model solves 15 out
of the 20 tasks with only 1000 training examples per task. Analysis of the
learned representations further demonstrates the ability of our model to encode
fine-grained entity information across a document.

31
The categories of this paper are: ao-sci
14
The categories of this paper are: math-ph
26
The categories of this paper are: bayes-an
17
The categories of this paper are: q-fin
22
The categories of this paper are: chao-dyn
23
The categories of this paper are: solv-int
3
The categories of this paper are: physics
8
The categories of this paper are: hep-ex
18
The categories of this paper are: econ
25
The categories of this paper are: patt-sol
4
The categories of this paper are: cond-mat
0
The categories of this paper are: hep-ph
35
The categories of this paper are: mtrl-th
16
The categories of this paper are: stat
10
The categories of this paper are: q-bio
11
The categories of this paper are: hep-lat