The task of joint dialog sentiment classification (DSC) and act recognition
(DAR) aims to simultaneously predict the sentiment label and act label for each
utterance in a dialog. In this paper, we put forward a new framework which
models the explicit dependencies via integrating \textit{prediction-level
interactions} other than semantics-level interactions, more consistent with
human intuition. Besides, we propose a speaker-aware temporal graph (SATG) and
a dual-task relational temporal graph (DRTG) to introduce \textit{temporal
relations} into dialog understanding and dual-task reasoning. To implement our
framework, we propose a novel model dubbed DARER, which first generates the
context-, speaker- and temporal-sensitive utterance representations via
modeling SATG, then conducts recurrent dual-task relational reasoning on DRTG,
in which process the estimated label distributions act as key clues in
prediction-level interactions. Experiment results show that DARER outperforms
existing models by large margins while requiring much less computation resource
and costing less training time. Remarkably, on DSC task in Mastodon, DARER
gains a relative improvement of about 25% over previous best model in terms of
F1, with less than 50% parameters and about only 60% required GPU memory.

该研究提出了一种新的框架和模型，通过引入预测级交互和时态表示，实现了联合对话情感分类和行为识别任务，与现有模型相比，性能更好，需要更少的计算资源和培训时间。