Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Gestalt: a Stacking Ensemble for SQuAD2.0   We propose a deep-learning system -- for the SQuAD2.0 task -- that finds, or
indicates the lack of, a correct answer to a question in a context paragraph.
Our goal is to learn an ensemble of heterogeneous SQuAD2.0 models that, when
blended properly, outperforms the best model in the ensemble per se. We created
a stacking ensemble that combines top-N predictions from two models, based on
ALBERT and RoBERTa, into a multiclass classification task to pick the best
answer out of their predictions. We explored various ensemble configurations,
input representations, and model architectures. For evaluation, we examined
test-set EM and F1 scores; our best-performing ensemble incorporated a
CNN-based meta-model and scored 87.117 and 90.306, respectively -- a relative
improvement of 0.55% for EM and 0.61% for F1 scores, compared to the baseline
performance of the best model in the ensemble, an ALBERT-based model, at 86.644
for EM and 89.760 for F1.

37
The categories of this paper are: supr-con
7
The categories of this paper are: hep-th
27
The categories of this paper are: comp-gas
6
The categories of this paper are: astro-ph
9
The categories of this paper are: nlin
13
The categories of this paper are: quant-ph
4
The categories of this paper are: cond-mat
22
The categories of this paper are: chao-dyn
35
The categories of this paper are: mtrl-th
29
The categories of this paper are: funct-an
8
The categories of this paper are: hep-ex
24
The categories of this paper are: dg-ga
2
The categories of this paper are: math
15
The categories of this paper are: nucl-ex
30
The categories of this paper are: q-alg
0
The categories of this paper are: hep-ph