Relational Temporal Graph Reasoning for Dual-task Dialogue Language
  Understanding
Bowen Xing Ivor W. Tsang
Dual-task dialog language understanding aims to tackle two correlative dialog
language understanding tasks simultaneously via leveraging their inherent
correlations. In this paper, we put forward a new framework, whose core is
relational temporal graph reasoning.We propose a speaker-aware temporal graph
(SATG) and a dual-task relational temporal graph (DRTG) to facilitate
relational temporal modeling in dialog understanding and dual-task reasoning.
Besides, different from previous works that only achieve implicit
semantics-level interactions, we propose to model the explicit dependencies via
integrating prediction-level interactions. To implement our framework, we first
propose a novel model Dual-tAsk temporal Relational rEcurrent Reasoning network
(DARER), which first generates the context-, speaker- and temporal-sensitive
utterance representations through relational temporal modeling of SATG, then
conducts recurrent dual-task relational temporal graph reasoning on DRTG, in
which process the estimated label distributions act as key clues in
prediction-level interactions. And the relational temporal modeling in DARER is
achieved by relational convolutional networks (RGCNs). Then we further propose
Relational Temporal Transformer (ReTeFormer), which achieves fine-grained
relational temporal modeling via Relation- and Structure-aware Disentangled
Multi-head Attention. Accordingly, we propose DARER with ReTeFormer (DARER2),
which adopts two variants of ReTeFormer to achieve the relational temporal
modeling of SATG and DTRG, respectively. The extensive experiments on different
scenarios verify that our models outperform state-of-the-art models by a large
margin. Remarkably, on the dialog sentiment classification task in the Mastodon
dataset, DARER and DARER2 gain relative improvements of about 28% and 34% over
the previous best model in terms of F1.