Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: DialogBERT: Discourse-Aware Response Generation via Learning to Recover
  and Rank Utterances   Recent advances in pre-trained language models have significantly improved
neural response generation. However, existing methods usually view the dialogue
context as a linear sequence of tokens and learn to generate the next word
through token-level self-attention. Such token-level encoding hinders the
exploration of discourse-level coherence among utterances. This paper presents
DialogBERT, a novel conversational response generation model that enhances
previous PLM-based dialogue models. DialogBERT employs a hierarchical
Transformer architecture. To efficiently capture the discourse-level coherence
among utterances, we propose two training objectives, including masked
utterance regression and distributed utterance order ranking in analogy to the
original BERT training. Experiments on three multi-turn conversation datasets
show that our approach remarkably outperforms the baselines, such as BART and
DialoGPT, in terms of quantitative evaluation. The human evaluation suggests
that DialogBERT generates more coherent, informative, and human-like responses
than the baselines with significant margins.

28
The categories of this paper are: alg-geom
14
The categories of this paper are: math-ph
37
The categories of this paper are: supr-con
29
The categories of this paper are: funct-an
32
The categories of this paper are: atom-ph
0
The categories of this paper are: hep-ph
16
The categories of this paper are: stat
8
The categories of this paper are: hep-ex
25
The categories of this paper are: patt-sol
3
The categories of this paper are: physics
4
The categories of this paper are: cond-mat
12
The categories of this paper are: nucl-th
21
The categories of this paper are: adap-org
9
The categories of this paper are: nlin
18
The categories of this paper are: econ
15
The categories of this paper are: nucl-ex