论文标题
部分可观测时空混沌系统的无模型预测
ASSIST: Towards Label Noise-Robust Dialogue State Tracking
论文作者
论文摘要
Multiwoz 2.0数据集大大提高了对话状态跟踪（DST）的研究。但是，在其状态注释中发现了大量噪音。这种噪音给训练DST模型带来了巨大的挑战。尽管最近出版了几种精致版本，包括Multiwoz 2.1-2.4，但仍然有很多嘈杂的标签，尤其是在培训集中。此外，纠正所有有问题的注释是昂贵的。在本文中，我们没有进一步提高注释质量，而是提出了一个名为Assist（标签噪声对话状态跟踪）的一般框架，以从嘈杂的标签中训练DST模型。 Assist首先使用在小型清洁数据集中训练的辅助模型为训练集中的每个样本生成伪标签，然后将生成的伪标签和香草噪声标签放在一起以训练主要模型。我们在理论上显示了辅助的有效性。实验结果还表明，与仅使用香草噪声标签相比，Multiwoz 2.0上的DST的联合目标准确性提高了多沃兹2.0的共同目标准确性，多沃兹2.0的共同目标准确性高达28.16 \％$ $。
The MultiWOZ 2.0 dataset has greatly boosted the research on dialogue state tracking (DST). However, substantial noise has been discovered in its state annotations. Such noise brings about huge challenges for training DST models robustly. Although several refined versions, including MultiWOZ 2.1-2.4, have been published recently, there are still lots of noisy labels, especially in the training set. Besides, it is costly to rectify all the problematic annotations. In this paper, instead of improving the annotation quality further, we propose a general framework, named ASSIST (lAbel noiSe-robuSt dIalogue State Tracking), to train DST models robustly from noisy labels. ASSIST first generates pseudo labels for each sample in the training set by using an auxiliary model trained on a small clean dataset, then puts the generated pseudo labels and vanilla noisy labels together to train the primary model. We show the validity of ASSIST theoretically. Experimental results also demonstrate that ASSIST improves the joint goal accuracy of DST by up to $28.16\%$ on MultiWOZ 2.0 and $8.41\%$ on MultiWOZ 2.4, compared to using only the vanilla noisy labels.
