Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Designing dialogue systems: A mean, grumpy, sarcastic chatbot in the
  browser   In this work we explore a deep learning-based dialogue system that generates
sarcastic and humorous responses from a conversation design perspective. We
trained a seq2seq model on a carefully curated dataset of 3000
question-answering pairs, the core of our mean, grumpy, sarcastic chatbot. We
show that end-to-end systems learn patterns very quickly from small datasets
and thus, are able to transfer simple linguistic structures representing
abstract concepts to unseen settings. We also deploy our LSTM-based
encoder-decoder model in the browser, where users can directly interact with
the chatbot. Human raters evaluated linguistic quality, creativity and
human-like traits, revealing the system's strengths, limitations and potential
for future research.

22
The categories of this paper are: chao-dyn
4
The categories of this paper are: cond-mat
21
The categories of this paper are: adap-org
2
The categories of this paper are: math
12
The categories of this paper are: nucl-th
28
The categories of this paper are: alg-geom
33
The categories of this paper are: plasm-ph
26
The categories of this paper are: bayes-an
10
The categories of this paper are: q-bio
3
The categories of this paper are: physics
34
The categories of this paper are: chem-ph
9
The categories of this paper are: nlin
24
The categories of this paper are: dg-ga
18
The categories of this paper are: econ
17
The categories of this paper are: q-fin
23
The categories of this paper are: solv-int