Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Robust Machine Comprehension Models via Adversarial Training   It is shown that many published models for the Stanford Question Answering
Dataset (Rajpurkar et al., 2016) lack robustness, suffering an over 50%
decrease in F1 score during adversarial evaluation based on the AddSent (Jia
and Liang, 2017) algorithm. It has also been shown that retraining models on
data generated by AddSent has limited effect on their robustness. We propose a
novel alternative adversary-generation algorithm, AddSentDiverse, that
significantly increases the variance within the adversarial training data by
providing effective examples that punish the model for making certain
superficial assumptions. Further, in order to improve robustness to AddSent's
semantic perturbations (e.g., antonyms), we jointly improve the model's
semantic-relationship learning capabilities in addition to our
AddSentDiverse-based adversarial training data augmentation. With these
additions, we show that we can make a state-of-the-art model significantly more
robust, achieving a 36.5% increase in F1 score under many different types of
adversarial evaluation while maintaining performance on the regular SQuAD task.

12
The categories of this paper are: nucl-th
27
The categories of this paper are: comp-gas
35
The categories of this paper are: mtrl-th
7
The categories of this paper are: hep-th
3
The categories of this paper are: physics
34
The categories of this paper are: chem-ph
20
The categories of this paper are: acc-phys
28
The categories of this paper are: alg-geom
14
The categories of this paper are: math-ph
30
The categories of this paper are: q-alg
4
The categories of this paper are: cond-mat
0
The categories of this paper are: hep-ph
11
The categories of this paper are: hep-lat
2
The categories of this paper are: math
18
The categories of this paper are: econ
24
The categories of this paper are: dg-ga