Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Modern Perspective on Query Likelihood with Deep Generative Retrieval
  Models   Existing neural ranking models follow the text matching paradigm, where
document-to-query relevance is estimated through predicting the matching score.
Drawing from the rich literature of classical generative retrieval models, we
introduce and formalize the paradigm of deep generative retrieval models
defined via the cumulative probabilities of generating query terms. This
paradigm offers a grounded probabilistic view on relevance estimation while
still enabling the use of modern neural architectures. In contrast to the
matching paradigm, the probabilistic nature of generative rankers readily
offers a fine-grained measure of uncertainty. We adopt several current neural
generative models in our framework and introduce a novel generative ranker
(T-PGN), which combines the encoding capacity of Transformers with the Pointer
Generator Network model. We conduct an extensive set of evaluation experiments
on passage retrieval, leveraging the MS MARCO Passage Re-ranking and TREC Deep
Learning 2019 Passage Re-ranking collections. Our results show the
significantly higher performance of the T-PGN model when compared with other
generative models. Lastly, we demonstrate that exploiting the uncertainty
information of deep generative rankers opens new perspectives to
query/collection understanding, and significantly improves the cut-off
prediction task.

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The categories of this paper are: astro-ph
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The categories of this paper are: adap-org
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The categories of this paper are: atom-ph
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The categories of this paper are: q-alg
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The categories of this paper are: econ
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The categories of this paper are: mtrl-th
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The categories of this paper are: hep-th
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The categories of this paper are: quant-ph
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The categories of this paper are: chem-ph
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The categories of this paper are: hep-lat
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The categories of this paper are: math-ph
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The categories of this paper are: solv-int
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The categories of this paper are: dg-ga
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The categories of this paper are: cmp-lg
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The categories of this paper are: hep-ph
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The categories of this paper are: q-bio