Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: ORB: An Open Reading Benchmark for Comprehensive Evaluation of Machine
  Reading Comprehension   Reading comprehension is one of the crucial tasks for furthering research in
natural language understanding. A lot of diverse reading comprehension datasets
have recently been introduced to study various phenomena in natural language,
ranging from simple paraphrase matching and entity typing to entity tracking
and understanding the implications of the context. Given the availability of
many such datasets, comprehensive and reliable evaluation is tedious and
time-consuming for researchers working on this problem. We present an
evaluation server, ORB, that reports performance on seven diverse reading
comprehension datasets, encouraging and facilitating testing a single model's
capability in understanding a wide variety of reading phenomena. The evaluation
server places no restrictions on how models are trained, so it is a suitable
test bed for exploring training paradigms and representation learning for
general reading facility. As more suitable datasets are released, they will be
added to the evaluation server. We also collect and include synthetic
augmentations for these datasets, testing how well models can handle
out-of-domain questions.

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The categories of this paper are: hep-ph
29
The categories of this paper are: funct-an
5
The categories of this paper are: gr-qc
37
The categories of this paper are: supr-con
13
The categories of this paper are: quant-ph
17
The categories of this paper are: q-fin
27
The categories of this paper are: comp-gas
32
The categories of this paper are: atom-ph
30
The categories of this paper are: q-alg
34
The categories of this paper are: chem-ph
9
The categories of this paper are: nlin
10
The categories of this paper are: q-bio
11
The categories of this paper are: hep-lat
25
The categories of this paper are: patt-sol
23
The categories of this paper are: solv-int
36
The categories of this paper are: cmp-lg