论文标题
TaskMix：用于口语意图理解的元学习数据增强
TaskMix: Data Augmentation for Meta-Learning of Spoken Intent Understanding
论文作者
论文摘要
元学习已成为一个研究方向，可以更好地将知识从相关任务转移到看不见但相关的任务。但是，元学习需要许多培训任务来学习将其转移到看不见的任务的表示形式。否则，它会导致过度拟合，并且性能比多任务学习更糟。我们表明，当任务多样性较低时，最先进的数据增强方法使这个过度拟合的问题恶化了。我们提出了一种简单的方法，即TaskMix，该方法通过线性插值现有任务来综合新任务。我们将TaskMix与内部多语言意图分类数据集进行了比较，该数据集的N-test ASR假设来自现实生活中的人机电话话语和两个来自MTOP的数据集。我们表明，TaskMix优于基准，在任务多样性较低时减轻过度拟合，即使在较高的情况下也不会降低性能。
Meta-Learning has emerged as a research direction to better transfer knowledge from related tasks to unseen but related tasks. However, Meta-Learning requires many training tasks to learn representations that transfer well to unseen tasks; otherwise, it leads to overfitting, and the performance degenerates to worse than Multi-task Learning. We show that a state-of-the-art data augmentation method worsens this problem of overfitting when the task diversity is low. We propose a simple method, TaskMix, which synthesizes new tasks by linearly interpolating existing tasks. We compare TaskMix against many baselines on an in-house multilingual intent classification dataset of N-Best ASR hypotheses derived from real-life human-machine telephony utterances and two datasets derived from MTOP. We show that TaskMix outperforms baselines, alleviates overfitting when task diversity is low, and does not degrade performance even when it is high.
