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.

本文提出了一种新的框架，其核心是关系时间图推理，通过利用其内在关联，同时解决两个相互关联的对话语言理解任务。通过集成预测级交互，我们还提出了不同于以往的显式依赖关系建模方法。通过对实验的验证，我们的模型在不同场景下都优于现有模型，尤其在灵长目数据集上的对话情感分类任务中，F1 分别比前一最佳模型提高了 28％和 34％。

双重任务对话语言理解的关系时序图推理

Relational Temporal Graph Reasoning for Dual-task Dialogue Language  Understanding

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。