Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Self- and Pseudo-self-supervised Prediction of Speaker and Key-utterance
  for Multi-party Dialogue Reading Comprehension   Multi-party dialogue machine reading comprehension (MRC) brings tremendous
challenge since it involves multiple speakers at one dialogue, resulting in
intricate speaker information flows and noisy dialogue contexts. To alleviate
such difficulties, previous models focus on how to incorporate these
information using complex graph-based modules and additional manually labeled
data, which is usually rare in real scenarios. In this paper, we design two
labour-free self- and pseudo-self-supervised prediction tasks on speaker and
key-utterance to implicitly model the speaker information flows, and capture
salient clues in a long dialogue. Experimental results on two benchmark
datasets have justified the effectiveness of our method over competitive
baselines and current state-of-the-art models.

20
The categories of this paper are: acc-phys
32
The categories of this paper are: atom-ph
13
The categories of this paper are: quant-ph
2
The categories of this paper are: math
21
The categories of this paper are: adap-org
36
The categories of this paper are: cmp-lg
15
The categories of this paper are: nucl-ex
22
The categories of this paper are: chao-dyn
27
The categories of this paper are: comp-gas
8
The categories of this paper are: hep-ex
18
The categories of this paper are: econ
11
The categories of this paper are: hep-lat
3
The categories of this paper are: physics
7
The categories of this paper are: hep-th
31
The categories of this paper are: ao-sci
16
The categories of this paper are: stat