Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Are we there yet? Encoder-decoder neural networks as cognitive models of
  English past tense inflection   The cognitive mechanisms needed to account for the English past tense have
long been a subject of debate in linguistics and cognitive science. Neural
network models were proposed early on, but were shown to have clear flaws.
Recently, however, Kirov and Cotterell (2018) showed that modern
encoder-decoder (ED) models overcome many of these flaws. They also presented
evidence that ED models demonstrate humanlike performance in a nonce-word task.
Here, we look more closely at the behaviour of their model in this task. We
find that (1) the model exhibits instability across multiple simulations in
terms of its correlation with human data, and (2) even when results are
aggregated across simulations (treating each simulation as an individual human
participant), the fit to the human data is not strong---worse than an older
rule-based model. These findings hold up through several alternative training
regimes and evaluation measures. Although other neural architectures might do
better, we conclude that there is still insufficient evidence to claim that
neural nets are a good cognitive model for this task.

31
The categories of this paper are: ao-sci
33
The categories of this paper are: plasm-ph
26
The categories of this paper are: bayes-an
34
The categories of this paper are: chem-ph
22
The categories of this paper are: chao-dyn
32
The categories of this paper are: atom-ph
0
The categories of this paper are: hep-ph
30
The categories of this paper are: q-alg
12
The categories of this paper are: nucl-th
2
The categories of this paper are: math
13
The categories of this paper are: quant-ph
23
The categories of this paper are: solv-int
36
The categories of this paper are: cmp-lg
21
The categories of this paper are: adap-org
4
The categories of this paper are: cond-mat
28
The categories of this paper are: alg-geom