Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Knowledge Bases with Parameters for Task-Oriented Dialogue
  Systems   Task-oriented dialogue systems are either modularized with separate dialogue
state tracking (DST) and management steps or end-to-end trainable. In either
case, the knowledge base (KB) plays an essential role in fulfilling user
requests. Modularized systems rely on DST to interact with the KB, which is
expensive in terms of annotation and inference time. End-to-end systems use the
KB directly as input, but they cannot scale when the KB is larger than a few
hundred entries. In this paper, we propose a method to embed the KB, of any
size, directly into the model parameters. The resulting model does not require
any DST or template responses, nor the KB as input, and it can dynamically
update its KB via fine-tuning. We evaluate our solution in five task-oriented
dialogue datasets with small, medium, and large KB size. Our experiments show
that end-to-end models can effectively embed knowledge bases in their
parameters and achieve competitive performance in all evaluated datasets.

32
The categories of this paper are: atom-ph
20
The categories of this paper are: acc-phys
30
The categories of this paper are: q-alg
8
The categories of this paper are: hep-ex
18
The categories of this paper are: econ
7
The categories of this paper are: hep-th
10
The categories of this paper are: q-bio
19
The categories of this paper are: eess
13
The categories of this paper are: quant-ph
5
The categories of this paper are: gr-qc
24
The categories of this paper are: dg-ga
0
The categories of this paper are: hep-ph
3
The categories of this paper are: physics
34
The categories of this paper are: chem-ph
26
The categories of this paper are: bayes-an
27
The categories of this paper are: comp-gas