  Conversational semantic role labeling (CSRL) is a newly proposed task that
uncovers the shallow semantic structures in a dialogue text. Unfortunately
several important characteristics of the CSRL task have been overlooked by the
existing works, such as the structural information integration, near-neighbor
influence. In this work, we investigate the integration of a latent graph for
CSRL. We propose to automatically induce a predicate-oriented latent graph
(POLar) with a predicate-centered Gaussian mechanism, by which the nearer and
informative words to the predicate will be allocated with more attention. The
POLar structure is then dynamically pruned and refined so as to best fit the
task need. We additionally introduce an effective dialogue-level pre-trained
language model, CoDiaBERT, for better supporting multiple utterance sentences
and handling the speaker coreference issue in CSRL. Our system outperforms
best-performing baselines on three benchmark CSRL datasets with big margins,
especially achieving over 4% F1 score improvements on the cross-utterance
argument detection. Further analyses are presented to better understand the
effectiveness of our proposed methods.
