Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Obtaining Faithful Interpretations from Compositional Neural Networks   Neural module networks (NMNs) are a popular approach for modeling
compositionality: they achieve high accuracy when applied to problems in
language and vision, while reflecting the compositional structure of the
problem in the network architecture. However, prior work implicitly assumed
that the structure of the network modules, describing the abstract reasoning
process, provides a faithful explanation of the model's reasoning; that is,
that all modules perform their intended behaviour. In this work, we propose and
conduct a systematic evaluation of the intermediate outputs of NMNs on NLVR2
and DROP, two datasets which require composing multiple reasoning steps. We
find that the intermediate outputs differ from the expected output,
illustrating that the network structure does not provide a faithful explanation
of model behaviour. To remedy that, we train the model with auxiliary
supervision and propose particular choices for module architecture that yield
much better faithfulness, at a minimal cost to accuracy.

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The categories of this paper are: math
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The categories of this paper are: hep-ph
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The categories of this paper are: atom-ph
34
The categories of this paper are: chem-ph
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The categories of this paper are: adap-org
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The categories of this paper are: dg-ga
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The categories of this paper are: q-alg
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The categories of this paper are: q-fin
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The categories of this paper are: q-bio
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The categories of this paper are: hep-th
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The categories of this paper are: ao-sci
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The categories of this paper are: hep-ex
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The categories of this paper are: eess
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The categories of this paper are: hep-lat
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The categories of this paper are: solv-int
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The categories of this paper are: mtrl-th