Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Combining Textual Content and Structure to Improve Dialog Similarity   Chatbots, taking advantage of the success of the messaging apps and recent
advances in Artificial Intelligence, have become very popular, from helping
business to improve customer services to chatting to users for the sake of
conversation and engagement (celebrity or personal bots). However, developing
and improving a chatbot requires understanding their data generated by its
users. Dialog data has a different nature of a simple question and answering
interaction, in which context and temporal properties (turn order) creates a
different understanding of such data. In this paper, we propose a novelty
metric to compute dialogs' similarity based not only on the text content but
also on the information related to the dialog structure. Our experimental
results performed over the Switchboard dataset show that using evidence from
both textual content and the dialog structure leads to more accurate results
than using each measure in isolation.

30
The categories of this paper are: q-alg
12
The categories of this paper are: nucl-th
35
The categories of this paper are: mtrl-th
37
The categories of this paper are: supr-con
11
The categories of this paper are: hep-lat
23
The categories of this paper are: solv-int
33
The categories of this paper are: plasm-ph
36
The categories of this paper are: cmp-lg
21
The categories of this paper are: adap-org
13
The categories of this paper are: quant-ph
14
The categories of this paper are: math-ph
4
The categories of this paper are: cond-mat
7
The categories of this paper are: hep-th
31
The categories of this paper are: ao-sci
29
The categories of this paper are: funct-an
0
The categories of this paper are: hep-ph