Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: GOLD: Improving Out-of-Scope Detection in Dialogues using Data
  Augmentation   Practical dialogue systems require robust methods of detecting out-of-scope
(OOS) utterances to avoid conversational breakdowns and related failure modes.
Directly training a model with labeled OOS examples yields reasonable
performance, but obtaining such data is a resource-intensive process. To tackle
this limited-data problem, previous methods focus on better modeling the
distribution of in-scope (INS) examples. We introduce GOLD as an orthogonal
technique that augments existing data to train better OOS detectors operating
in low-data regimes. GOLD generates pseudo-labeled candidates using samples
from an auxiliary dataset and keeps only the most beneficial candidates for
training through a novel filtering mechanism. In experiments across three
target benchmarks, the top GOLD model outperforms all existing methods on all
key metrics, achieving relative gains of 52.4%, 48.9% and 50.3% against median
baseline performance. We also analyze the unique properties of OOS data to
identify key factors for optimally applying our proposed method.

37
The categories of this paper are: supr-con
6
The categories of this paper are: astro-ph
12
The categories of this paper are: nucl-th
21
The categories of this paper are: adap-org
13
The categories of this paper are: quant-ph
7
The categories of this paper are: hep-th
28
The categories of this paper are: alg-geom
24
The categories of this paper are: dg-ga
2
The categories of this paper are: math
23
The categories of this paper are: solv-int
11
The categories of this paper are: hep-lat
17
The categories of this paper are: q-fin
9
The categories of this paper are: nlin
18
The categories of this paper are: econ
35
The categories of this paper are: mtrl-th
20
The categories of this paper are: acc-phys