论文标题
DARER：联合对话情感分类和ACT识别的双任务时间关系反复推理网络
DARER: Dual-task Temporal Relational Recurrent Reasoning Network for Joint Dialog Sentiment Classification and Act Recognition
论文作者
论文摘要
联合对话情感分类（DSC）和ACT识别（DAR）的任务旨在同时预测对话中每个话语的情感标签和ACT标签。在本文中，我们提出了一个新的框架，该框架通过集成\ textit {预测级交互}来模拟明确的依赖性，而不是语义级别的交互，与人类的直觉更一致。此外，我们建议使用说话者感知的时间图（SATG）和双任务关系时间图（DRTG），以将\ textit {暂时关系}引入对话框理解和双任务推理中。为了实施我们的框架，我们提出了一种名为Darer的新颖模型，该模型首先通过建模SATG来生成上下文，说话者和时间敏感的话语表示，然后对DRTG进行经常性的双任务关系推理，在此过程中，估计的标签分布在预测 - 级别的相互作用中是关键的线索。实验结果表明，DARER以大幅度的优于现有模型，同时需要更少的计算资源，而培训时间则更少。值得注意的是，在Mastodon的DSC任务上，Darer在F1方面的相对最佳模型比以前的最佳模型获得了约25％的相对提高，其参数少于50％，只有大约60％的GPU内存。
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.
