Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Exploring Semi-Supervised Learning for Predicting Listener Backchannels   Developing human-like conversational agents is a prime area in HCI research
and subsumes many tasks. Predicting listener backchannels is one such
actively-researched task. While many studies have used different approaches for
backchannel prediction, they all have depended on manual annotations for a
large dataset. This is a bottleneck impacting the scalability of development.
To this end, we propose using semi-supervised techniques to automate the
process of identifying backchannels, thereby easing the annotation process. To
analyze our identification module's feasibility, we compared the backchannel
prediction models trained on (a) manually-annotated and (b) semi-supervised
labels. Quantitative analysis revealed that the proposed semi-supervised
approach could attain 95% of the former's performance. Our user-study findings
revealed that almost 60% of the participants found the backchannel responses
predicted by the proposed model more natural. Finally, we also analyzed the
impact of personality on the type of backchannel signals and validated our
findings in the user-study.

29
The categories of this paper are: funct-an
14
The categories of this paper are: math-ph
12
The categories of this paper are: nucl-th
25
The categories of this paper are: patt-sol
16
The categories of this paper are: stat
2
The categories of this paper are: math
30
The categories of this paper are: q-alg
37
The categories of this paper are: supr-con
27
The categories of this paper are: comp-gas
32
The categories of this paper are: atom-ph
23
The categories of this paper are: solv-int
34
The categories of this paper are: chem-ph
22
The categories of this paper are: chao-dyn
11
The categories of this paper are: hep-lat
5
The categories of this paper are: gr-qc
35
The categories of this paper are: mtrl-th