Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Text-to-Text Multi-view Learning for Passage Re-ranking   Recently, much progress in natural language processing has been driven by
deep contextualized representations pretrained on large corpora. Typically, the
fine-tuning on these pretrained models for a specific downstream task is based
on single-view learning, which is however inadequate as a sentence can be
interpreted differently from different perspectives. Therefore, in this work,
we propose a text-to-text multi-view learning framework by incorporating an
additional view -- the text generation view -- into a typical single-view
passage ranking model. Empirically, the proposed approach is of help to the
ranking performance compared to its single-view counterpart. Ablation studies
are also reported in the paper.

15
The categories of this paper are: nucl-ex
32
The categories of this paper are: atom-ph
16
The categories of this paper are: stat
36
The categories of this paper are: cmp-lg
3
The categories of this paper are: physics
18
The categories of this paper are: econ
12
The categories of this paper are: nucl-th
27
The categories of this paper are: comp-gas
7
The categories of this paper are: hep-th
8
The categories of this paper are: hep-ex
9
The categories of this paper are: nlin
26
The categories of this paper are: bayes-an
37
The categories of this paper are: supr-con
2
The categories of this paper are: math
10
The categories of this paper are: q-bio
6
The categories of this paper are: astro-ph