Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Likelihood Ratios and Generative Classifiers for Unsupervised
  Out-of-Domain Detection In Task Oriented Dialog   The task of identifying out-of-domain (OOD) input examples directly at
test-time has seen renewed interest recently due to increased real world
deployment of models. In this work, we focus on OOD detection for natural
language sentence inputs to task-based dialog systems. Our findings are
three-fold: First, we curate and release ROSTD (Real Out-of-Domain Sentences
From Task-oriented Dialog) - a dataset of 4K OOD examples for the publicly
available dataset from (Schuster et al. 2019). In contrast to existing settings
which synthesize OOD examples by holding out a subset of classes, our examples
were authored by annotators with apriori instructions to be out-of-domain with
respect to the sentences in an existing dataset. Second, we explore likelihood
ratio based approaches as an alternative to currently prevalent paradigms.
Specifically, we reformulate and apply these approaches to natural language
inputs. We find that they match or outperform the latter on all datasets, with
larger improvements on non-artificial OOD benchmarks such as our dataset. Our
ablations validate that specifically using likelihood ratios rather than plain
likelihood is necessary to discriminate well between OOD and in-domain data.
Third, we propose learning a generative classifier and computing a marginal
likelihood (ratio) for OOD detection. This allows us to use a principled
likelihood while at the same time exploiting training-time labels. We find that
this approach outperforms both simple likelihood (ratio) based and other prior
approaches. We are hitherto the first to investigate the use of generative
classifiers for OOD detection at test-time.

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The categories of this paper are: plasm-ph
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The categories of this paper are: solv-int
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The categories of this paper are: hep-lat
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The categories of this paper are: atom-ph
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The categories of this paper are: patt-sol
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The categories of this paper are: cond-mat
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The categories of this paper are: dg-ga
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The categories of this paper are: mtrl-th
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The categories of this paper are: math
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The categories of this paper are: astro-ph
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The categories of this paper are: nucl-th
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The categories of this paper are: chem-ph
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The categories of this paper are: comp-gas
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The categories of this paper are: econ
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The categories of this paper are: cmp-lg
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The categories of this paper are: q-fin