Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Ask Me Anything: Dynamic Memory Networks for Natural Language Processing   Most tasks in natural language processing can be cast into question answering
(QA) problems over language input. We introduce the dynamic memory network
(DMN), a neural network architecture which processes input sequences and
questions, forms episodic memories, and generates relevant answers. Questions
trigger an iterative attention process which allows the model to condition its
attention on the inputs and the result of previous iterations. These results
are then reasoned over in a hierarchical recurrent sequence model to generate
answers. The DMN can be trained end-to-end and obtains state-of-the-art results
on several types of tasks and datasets: question answering (Facebook's bAbI
dataset), text classification for sentiment analysis (Stanford Sentiment
Treebank) and sequence modeling for part-of-speech tagging (WSJ-PTB). The
training for these different tasks relies exclusively on trained word vector
representations and input-question-answer triplets.

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The categories of this paper are: acc-phys
13
The categories of this paper are: quant-ph
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The categories of this paper are: supr-con
24
The categories of this paper are: dg-ga
28
The categories of this paper are: alg-geom
9
The categories of this paper are: nlin
0
The categories of this paper are: hep-ph
8
The categories of this paper are: hep-ex
35
The categories of this paper are: mtrl-th
2
The categories of this paper are: math
22
The categories of this paper are: chao-dyn
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The categories of this paper are: hep-lat
23
The categories of this paper are: solv-int
3
The categories of this paper are: physics
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The categories of this paper are: adap-org
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The categories of this paper are: eess