Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Measuring Compositional Generalization: A Comprehensive Method on
  Realistic Data   State-of-the-art machine learning methods exhibit limited compositional
generalization. At the same time, there is a lack of realistic benchmarks that
comprehensively measure this ability, which makes it challenging to find and
evaluate improvements. We introduce a novel method to systematically construct
such benchmarks by maximizing compound divergence while guaranteeing a small
atom divergence between train and test sets, and we quantitatively compare this
method to other approaches for creating compositional generalization
benchmarks. We present a large and realistic natural language question
answering dataset that is constructed according to this method, and we use it
to analyze the compositional generalization ability of three machine learning
architectures. We find that they fail to generalize compositionally and that
there is a surprisingly strong negative correlation between compound divergence
and accuracy. We also demonstrate how our method can be used to create new
compositionality benchmarks on top of the existing SCAN dataset, which confirms
these findings.

2
The categories of this paper are: math
20
The categories of this paper are: acc-phys
22
The categories of this paper are: chao-dyn
13
The categories of this paper are: quant-ph
10
The categories of this paper are: q-bio
31
The categories of this paper are: ao-sci
6
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
37
The categories of this paper are: supr-con
17
The categories of this paper are: q-fin
27
The categories of this paper are: comp-gas
32
The categories of this paper are: atom-ph
23
The categories of this paper are: solv-int
34
The categories of this paper are: chem-ph
3
The categories of this paper are: physics