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.

翻译：双任务对话语言理解旨在通过利用两个关联对话语言理解任务之间的内在相关性，同时处理这两个任务。本文提出了一种新框架，其核心是关系时序图推理。我们提出了说话者感知时序图（SATG）和双任务关系时序图（DRTG），以促进对话理解中的关系时序建模及双任务推理。此外，不同于以往仅实现隐式语义层交互的工作，我们提出通过整合预测层交互来建模显式依赖关系。为实现该框架，我们首先提出了一种新颖的模型——双任务时序关系循环推理网络（DARER），该模型首先通过SATG的关系时序建模生成上下文、说话者和时序敏感的语句表示，随后在DRTG上进行循环双任务关系时序图推理，在此过程中估计的标签分布作为预测层交互的关键线索。DARER中的关系时序建模通过关系卷积网络（RGCNs）实现。接着，我们进一步提出了关系时序变换器（ReTeFormer），通过关系和结构感知的解耦多头注意力实现细粒度关系时序建模。据此，我们提出了基于ReTeFormer的DARER（DARER²），采用两种ReTeFormer变体分别实现SATG和DTG的关系时序建模。在不同场景下的广泛实验验证，我们的模型显著优于现有最优模型。值得注意的是，在Mastodon数据集的对话情感分类任务中，DARER和DARER²相比之前最优模型的F1值分别提升了约28%和34%。