Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Unleashing the Power of Neural Discourse Parsers -- A Context and
  Structure Aware Approach Using Large Scale Pretraining   RST-based discourse parsing is an important NLP task with numerous downstream
applications, such as summarization, machine translation and opinion mining. In
this paper, we demonstrate a simple, yet highly accurate discourse parser,
incorporating recent contextual language models. Our parser establishes the new
state-of-the-art (SOTA) performance for predicting structure and nuclearity on
two key RST datasets, RST-DT and Instr-DT. We further demonstrate that
pretraining our parser on the recently available large-scale "silver-standard"
discourse treebank MEGA-DT provides even larger performance benefits,
suggesting a novel and promising research direction in the field of discourse
analysis.

0
The categories of this paper are: hep-ph
11
The categories of this paper are: hep-lat
15
The categories of this paper are: nucl-ex
7
The categories of this paper are: hep-th
9
The categories of this paper are: nlin
21
The categories of this paper are: adap-org
24
The categories of this paper are: dg-ga
28
The categories of this paper are: alg-geom
30
The categories of this paper are: q-alg
33
The categories of this paper are: plasm-ph
25
The categories of this paper are: patt-sol
34
The categories of this paper are: chem-ph
36
The categories of this paper are: cmp-lg
8
The categories of this paper are: hep-ex
35
The categories of this paper are: mtrl-th
23
The categories of this paper are: solv-int