Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Differentiable Reasoning over a Virtual Knowledge Base   We consider the task of answering complex multi-hop questions using a corpus
as a virtual knowledge base (KB). In particular, we describe a neural module,
DrKIT, that traverses textual data like a KB, softly following paths of
relations between mentions of entities in the corpus. At each step the module
uses a combination of sparse-matrix TFIDF indices and a maximum inner product
search (MIPS) on a special index of contextual representations of the mentions.
This module is differentiable, so the full system can be trained end-to-end
using gradient based methods, starting from natural language inputs. We also
describe a pretraining scheme for the contextual representation encoder by
generating hard negative examples using existing knowledge bases. We show that
DrKIT improves accuracy by 9 points on 3-hop questions in the MetaQA dataset,
cutting the gap between text-based and KB-based state-of-the-art by 70%. On
HotpotQA, DrKIT leads to a 10% improvement over a BERT-based re-ranking
approach to retrieving the relevant passages required to answer a question.
DrKIT is also very efficient, processing 10-100x more queries per second than
existing multi-hop systems.

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The categories of this paper are: q-fin
29
The categories of this paper are: funct-an
10
The categories of this paper are: q-bio
35
The categories of this paper are: mtrl-th
0
The categories of this paper are: hep-ph
25
The categories of this paper are: patt-sol
36
The categories of this paper are: cmp-lg
33
The categories of this paper are: plasm-ph
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The categories of this paper are: adap-org
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The categories of this paper are: hep-lat
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The categories of this paper are: supr-con
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The categories of this paper are: alg-geom
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The categories of this paper are: econ
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The categories of this paper are: hep-th
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The categories of this paper are: dg-ga
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The categories of this paper are: chem-ph