Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: MinTL: Minimalist Transfer Learning for Task-Oriented Dialogue Systems   In this paper, we propose Minimalist Transfer Learning (MinTL) to simplify
the system design process of task-oriented dialogue systems and alleviate the
over-dependency on annotated data. MinTL is a simple yet effective transfer
learning framework, which allows us to plug-and-play pre-trained seq2seq
models, and jointly learn dialogue state tracking and dialogue response
generation. Unlike previous approaches, which use a copy mechanism to
"carryover" the old dialogue states to the new one, we introduce Levenshtein
belief spans (Lev), that allows efficient dialogue state tracking with a
minimal generation length. We instantiate our learning framework with two
pre-trained backbones: T5 and BART, and evaluate them on MultiWOZ. Extensive
experiments demonstrate that: 1) our systems establish new state-of-the-art
results on end-to-end response generation, 2) MinTL-based systems are more
robust than baseline methods in the low resource setting, and they achieve
competitive results with only 20\% training data, and 3) Lev greatly improves
the inference efficiency.

18
The categories of this paper are: econ
12
The categories of this paper are: nucl-th
9
The categories of this paper are: nlin
21
The categories of this paper are: adap-org
20
The categories of this paper are: acc-phys
0
The categories of this paper are: hep-ph
11
The categories of this paper are: hep-lat
4
The categories of this paper are: cond-mat
35
The categories of this paper are: mtrl-th
25
The categories of this paper are: patt-sol
27
The categories of this paper are: comp-gas
28
The categories of this paper are: alg-geom
37
The categories of this paper are: supr-con
24
The categories of this paper are: dg-ga
10
The categories of this paper are: q-bio
16
The categories of this paper are: stat