Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Examining the rhetorical capacities of neural language models   Recently, neural language models (LMs) have demonstrated impressive abilities
in generating high-quality discourse. While many recent papers have analyzed
the syntactic aspects encoded in LMs, there has been no analysis to date of the
inter-sentential, rhetorical knowledge. In this paper, we propose a method that
quantitatively evaluates the rhetorical capacities of neural LMs. We examine
the capacities of neural LMs understanding the rhetoric of discourse by
evaluating their abilities to encode a set of linguistic features derived from
Rhetorical Structure Theory (RST). Our experiments show that BERT-based LMs
outperform other Transformer LMs, revealing the richer discourse knowledge in
their intermediate layer representations. In addition, GPT-2 and XLNet
apparently encode less rhetorical knowledge, and we suggest an explanation
drawing from linguistic philosophy. Our method shows an avenue towards
quantifying the rhetorical capacities of neural LMs.

33
The categories of this paper are: plasm-ph
28
The categories of this paper are: alg-geom
34
The categories of this paper are: chem-ph
5
The categories of this paper are: gr-qc
8
The categories of this paper are: hep-ex
7
The categories of this paper are: hep-th
20
The categories of this paper are: acc-phys
13
The categories of this paper are: quant-ph
37
The categories of this paper are: supr-con
3
The categories of this paper are: physics
30
The categories of this paper are: q-alg
24
The categories of this paper are: dg-ga
36
The categories of this paper are: cmp-lg
23
The categories of this paper are: solv-int
16
The categories of this paper are: stat
19
The categories of this paper are: eess