Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: LSTM-based Deep Learning Models for Non-factoid Answer Selection   In this paper, we apply a general deep learning (DL) framework for the answer
selection task, which does not depend on manually defined features or
linguistic tools. The basic framework is to build the embeddings of questions
and answers based on bidirectional long short-term memory (biLSTM) models, and
measure their closeness by cosine similarity. We further extend this basic
model in two directions. One direction is to define a more composite
representation for questions and answers by combining convolutional neural
network with the basic framework. The other direction is to utilize a simple
but efficient attention mechanism in order to generate the answer
representation according to the question context. Several variations of models
are provided. The models are examined by two datasets, including TREC-QA and
InsuranceQA. Experimental results demonstrate that the proposed models
substantially outperform several strong baselines.

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The categories of this paper are: funct-an
5
The categories of this paper are: gr-qc
10
The categories of this paper are: q-bio
32
The categories of this paper are: atom-ph
11
The categories of this paper are: hep-lat
36
The categories of this paper are: cmp-lg
7
The categories of this paper are: hep-th
16
The categories of this paper are: stat
0
The categories of this paper are: hep-ph
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The categories of this paper are: chem-ph
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The categories of this paper are: patt-sol
20
The categories of this paper are: acc-phys
18
The categories of this paper are: econ
35
The categories of this paper are: mtrl-th
12
The categories of this paper are: nucl-th
23
The categories of this paper are: solv-int