Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning-to-Rank with BERT in TF-Ranking   This paper describes a machine learning algorithm for document (re)ranking,
in which queries and documents are firstly encoded using BERT [1], and on top
of that a learning-to-rank (LTR) model constructed with TF-Ranking (TFR) [2] is
applied to further optimize the ranking performance. This approach is proved to
be effective in a public MS MARCO benchmark [3]. Our first two submissions
achieve the best performance for the passage re-ranking task [4], and the
second best performance for the passage full-ranking task as of April 10, 2020
[5]. To leverage the lately development of pre-trained language models, we
recently integrate RoBERTa [6] and ELECTRA [7]. Our latest submissions improve
our previously state-of-the-art re-ranking performance by 4.3% [8], and achieve
the third best performance for the full-ranking task [9] as of June 8, 2020.
Both of them demonstrate the effectiveness of combining ranking losses with
BERT representations for document ranking.

22
The categories of this paper are: chao-dyn
11
The categories of this paper are: hep-lat
5
The categories of this paper are: gr-qc
27
The categories of this paper are: comp-gas
37
The categories of this paper are: supr-con
2
The categories of this paper are: math
13
The categories of this paper are: quant-ph
36
The categories of this paper are: cmp-lg
7
The categories of this paper are: hep-th
20
The categories of this paper are: acc-phys
9
The categories of this paper are: nlin
10
The categories of this paper are: q-bio
24
The categories of this paper are: dg-ga
3
The categories of this paper are: physics
35
The categories of this paper are: mtrl-th
28
The categories of this paper are: alg-geom