Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Overestimation of Syntactic Representationin Neural Language Models   With the advent of powerful neural language models over the last few years,
research attention has increasingly focused on what aspects of language they
represent that make them so successful. Several testing methodologies have been
developed to probe models' syntactic representations. One popular method for
determining a model's ability to induce syntactic structure trains a model on
strings generated according to a template then tests the model's ability to
distinguish such strings from superficially similar ones with different syntax.
We illustrate a fundamental problem with this approach by reproducing positive
results from a recent paper with two non-syntactic baseline language models: an
n-gram model and an LSTM model trained on scrambled inputs.

20
The categories of this paper are: acc-phys
13
The categories of this paper are: quant-ph
17
The categories of this paper are: q-fin
32
The categories of this paper are: atom-ph
6
The categories of this paper are: astro-ph
36
The categories of this paper are: cmp-lg
33
The categories of this paper are: plasm-ph
4
The categories of this paper are: cond-mat
21
The categories of this paper are: adap-org
3
The categories of this paper are: physics
11
The categories of this paper are: hep-lat
7
The categories of this paper are: hep-th
31
The categories of this paper are: ao-sci
18
The categories of this paper are: econ
37
The categories of this paper are: supr-con
16
The categories of this paper are: stat