Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Data Augmentation for BERT Fine-Tuning in Open-Domain Question Answering   Recently, a simple combination of passage retrieval using off-the-shelf IR
techniques and a BERT reader was found to be very effective for question
answering directly on Wikipedia, yielding a large improvement over the previous
state of the art on a standard benchmark dataset. In this paper, we present a
data augmentation technique using distant supervision that exploits positive as
well as negative examples. We apply a stage-wise approach to fine tuning BERT
on multiple datasets, starting with data that is "furthest" from the test data
and ending with the "closest". Experimental results show large gains in
effectiveness over previous approaches on English QA datasets, and we establish
new baselines on two recent Chinese QA datasets.

28
The categories of this paper are: alg-geom
19
The categories of this paper are: eess
22
The categories of this paper are: chao-dyn
20
The categories of this paper are: acc-phys
15
The categories of this paper are: nucl-ex
9
The categories of this paper are: nlin
6
The categories of this paper are: astro-ph
23
The categories of this paper are: solv-int
11
The categories of this paper are: hep-lat
27
The categories of this paper are: comp-gas
5
The categories of this paper are: gr-qc
33
The categories of this paper are: plasm-ph
18
The categories of this paper are: econ
24
The categories of this paper are: dg-ga
12
The categories of this paper are: nucl-th
29
The categories of this paper are: funct-an