Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Augmenting Compositional Models for Knowledge Base Completion Using
  Gradient Representations   Neural models of Knowledge Base data have typically employed compositional
representations of graph objects: entity and relation embeddings are
systematically combined to evaluate the truth of a candidate Knowedge Base
entry. Using a model inspired by Harmonic Grammar, we propose to tokenize
triplet embeddings by subjecting them to a process of optimization with respect
to learned well-formedness conditions on Knowledge Base triplets. The resulting
model, known as Gradient Graphs, leads to sizable improvements when implemented
as a companion to compositional models. Also, we show that the
"supracompositional" triplet token embeddings it produces have interpretable
properties that prove helpful in performing inference on the resulting triplet
representations.

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The categories of this paper are: eess
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The categories of this paper are: astro-ph
36
The categories of this paper are: cmp-lg
0
The categories of this paper are: hep-ph
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The categories of this paper are: math-ph
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The categories of this paper are: hep-lat
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The categories of this paper are: ao-sci
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The categories of this paper are: stat
29
The categories of this paper are: funct-an
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The categories of this paper are: adap-org
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The categories of this paper are: quant-ph
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The categories of this paper are: alg-geom
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The categories of this paper are: patt-sol
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The categories of this paper are: gr-qc
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The categories of this paper are: nucl-th
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The categories of this paper are: hep-ex