Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Overview of the TREC 2020 deep learning track   This is the second year of the TREC Deep Learning Track, with the goal of
studying ad hoc ranking in the large training data regime. We again have a
document retrieval task and a passage retrieval task, each with hundreds of
thousands of human-labeled training queries. We evaluate using single-shot
TREC-style evaluation, to give us a picture of which ranking methods work best
when large data is available, with much more comprehensive relevance labeling
on the small number of test queries. This year we have further evidence that
rankers with BERT-style pretraining outperform other rankers in the large data
regime.

5
The categories of this paper are: gr-qc
34
The categories of this paper are: chem-ph
37
The categories of this paper are: supr-con
28
The categories of this paper are: alg-geom
33
The categories of this paper are: plasm-ph
29
The categories of this paper are: funct-an
14
The categories of this paper are: math-ph
7
The categories of this paper are: hep-th
11
The categories of this paper are: hep-lat
10
The categories of this paper are: q-bio
27
The categories of this paper are: comp-gas
25
The categories of this paper are: patt-sol
6
The categories of this paper are: astro-ph
0
The categories of this paper are: hep-ph
21
The categories of this paper are: adap-org
32
The categories of this paper are: atom-ph