Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Topic-aware chatbot using Recurrent Neural Networks and Nonnegative
  Matrix Factorization   We propose a novel model for a topic-aware chatbot by combining the
traditional Recurrent Neural Network (RNN) encoder-decoder model with a topic
attention layer based on Nonnegative Matrix Factorization (NMF). After learning
topic vectors from an auxiliary text corpus via NMF, the decoder is trained so
that it is more likely to sample response words from the most correlated topic
vectors. One of the main advantages in our architecture is that the user can
easily switch the NMF-learned topic vectors so that the chatbot obtains desired
topic-awareness. We demonstrate our model by training on a single
conversational data set which is then augmented with topic matrices learned
from different auxiliary data sets. We show that our topic-aware chatbot not
only outperforms the non-topic counterpart, but also that each topic-aware
model qualitatively and contextually gives the most relevant answer depending
on the topic of question.

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The categories of this paper are: eess
10
The categories of this paper are: q-bio
12
The categories of this paper are: nucl-th
36
The categories of this paper are: cmp-lg
25
The categories of this paper are: patt-sol
20
The categories of this paper are: acc-phys
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The categories of this paper are: alg-geom
6
The categories of this paper are: astro-ph
5
The categories of this paper are: gr-qc
27
The categories of this paper are: comp-gas
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The categories of this paper are: hep-ex
11
The categories of this paper are: hep-lat
34
The categories of this paper are: chem-ph
7
The categories of this paper are: hep-th
26
The categories of this paper are: bayes-an
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The categories of this paper are: q-fin