Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Toward Continual Learning for Conversational Agents   While end-to-end neural conversation models have led to promising advances in
reducing hand-crafted features and errors induced by the traditional complex
system architecture, they typically require an enormous amount of data due to
the lack of modularity. Previous studies adopted a hybrid approach with
knowledge-based components either to abstract out domain-specific information
or to augment data to cover more diverse patterns. On the contrary, we propose
to directly address the problem using recent developments in the space of
continual learning for neural models. Specifically, we adopt a
domain-independent neural conversational model and introduce a novel neural
continual learning algorithm that allows a conversational agent to accumulate
skills across different tasks in a data-efficient way. To the best of our
knowledge, this is the first work that applies continual learning to
conversation systems. We verified the efficacy of our method through a
conversational skill transfer from either synthetic dialogs or human-human
dialogs to human-computer conversations in a customer support domain.

11
The categories of this paper are: hep-lat
10
The categories of this paper are: q-bio
21
The categories of this paper are: adap-org
23
The categories of this paper are: solv-int
30
The categories of this paper are: q-alg
37
The categories of this paper are: supr-con
7
The categories of this paper are: hep-th
20
The categories of this paper are: acc-phys
8
The categories of this paper are: hep-ex
3
The categories of this paper are: physics
14
The categories of this paper are: math-ph
36
The categories of this paper are: cmp-lg
2
The categories of this paper are: math
34
The categories of this paper are: chem-ph
13
The categories of this paper are: quant-ph
24
The categories of this paper are: dg-ga