Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Compositionality as Lexical Symmetry   Standard deep network models lack the inductive biases needed to generalize
compositionally in tasks like semantic parsing, translation, and question
answering. A large body of work in natural language processing seeks to
overcome this limitation with new model architectures that enforce a
compositional process of sentence interpretation. In this paper, we present a
domain-general framework for compositional modeling that instead formulates
compositionality as a constraint on data distributions. We prove that for any
task factorizable into a lexicon and a composition function, there exists a
family of data transformation functions that are guaranteed to produce new,
well-formed examples when applied to training data. We further show that it is
possible to identify these data transformations even when the composition
function is unknown (e.g. when we do not know how to write or infer a symbolic
grammar). Using these transformation functions to perform data augmentation for
ordinary RNN and transformer sequence models, we obtain state-of-the-art
results on the CLEVR-CoGenT visual question answering dataset, and results
comparable to specialized model architectures on the COGS semantic parsing
dataset.

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The categories of this paper are: quant-ph
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The categories of this paper are: adap-org
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The categories of this paper are: stat
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The categories of this paper are: physics
29
The categories of this paper are: funct-an
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The categories of this paper are: comp-gas
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The categories of this paper are: cond-mat
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The categories of this paper are: astro-ph
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The categories of this paper are: plasm-ph
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The categories of this paper are: bayes-an
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The categories of this paper are: hep-ph
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The categories of this paper are: q-bio
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The categories of this paper are: mtrl-th
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The categories of this paper are: nlin
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The categories of this paper are: math
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The categories of this paper are: hep-th