Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: When in Doubt, Ask: Generating Answerable and Unanswerable Questions,
  Unsupervised   Question Answering (QA) is key for making possible a robust communication
between human and machine. Modern language models used for QA have surpassed
the human-performance in several essential tasks; however, these models require
large amounts of human-generated training data which are costly and
time-consuming to create. This paper studies augmenting human-made datasets
with synthetic data as a way of surmounting this problem. A state-of-the-art
model based on deep transformers is used to inspect the impact of using
synthetic answerable and unanswerable questions to complement a well-known
human-made dataset. The results indicate a tangible improvement in the
performance of the language model (measured in terms of F1 and EM scores)
trained on the mixed dataset. Specifically, unanswerable question-answers prove
more effective in boosting the model: the F1 score gain from adding to the
original dataset the answerable, unanswerable, and combined question-answers
were 1.3%, 5.0%, and 6.7%, respectively. [Link to the Github repository:
https://github.com/lnikolenko/EQA]

16
The categories of this paper are: stat
35
The categories of this paper are: mtrl-th
18
The categories of this paper are: econ
0
The categories of this paper are: hep-ph
17
The categories of this paper are: q-fin
33
The categories of this paper are: plasm-ph
7
The categories of this paper are: hep-th
26
The categories of this paper are: bayes-an
20
The categories of this paper are: acc-phys
4
The categories of this paper are: cond-mat
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The categories of this paper are: eess
36
The categories of this paper are: cmp-lg
27
The categories of this paper are: comp-gas
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The categories of this paper are: nlin
6
The categories of this paper are: astro-ph
11
The categories of this paper are: hep-lat