  While multi-party conversations are often less structured than monologues and
documents, they are implicitly organized by semantic level correlations across
the interactive turns, and dialogue discourse analysis can be applied to
predict the dependency structure and relations between the elementary discourse
units, and provide feature-rich structural information for downstream tasks.
However, the existing corpora with dialogue discourse annotation are collected
from specific domains with limited sample sizes, rendering the performance of
data-driven approaches poor on incoming dialogues without any domain
adaptation. In this paper, we first introduce a Transformer-based parser, and
assess its cross-domain performance. We next adopt three methods to gain domain
integration from both data and language modeling perspectives to improve the
generalization capability. Empirical results show that the neural parser can
benefit from our proposed methods, and performs better on cross-domain dialogue
samples.

Computation and Language