KnowMAN：弱监督多项对抗网络
KnowMAN: Weakly Supervised Multinomial Adversarial Networks
摘要：
缺乏用于训练神经模型的标记数据通常通过利用有关特定任务的知识来解决，从而产生启发式但嘈杂的标签。知识被捕获在标记函数中，标记函数检测训练样本中的某些特征或模式，并注释相应的标签以进行训练。这种弱监督训练的过程可能导致过度依赖标记函数捕获的信号，并阻碍模型利用其他信号或很好地泛化。我们提出了KnowMAN，这是一种对抗性方案，可以控制与特定标记功能相关的信号的影响。KnowMAN迫使网络学习对这些信号不变的表示，并拾取与输出标签更普遍相关的其他信号。与使用预训练的Transformer语言模型和基于特征的基线的直接弱监督学习相比，KnowMAN大大改善了结果。
英文摘要：
The absence of labeled data for training neural models is often addressed by leveraging knowledge about the specific task, resulting in heuristic but noisy labels. The knowledge is captured in labeling functions, which detect certain regularities or patterns in the training samples and annotate corresponding labels for training. This process of weakly supervised training may result in an over-reliance on the signals captured by the labeling functions and hinder models to exploit other signals or to generalize well. We propose KnowMAN, an adversarial scheme that enables to control influence of signals associated with specific labeling functions. KnowMAN forces the network to learn representations that are invariant to those signals and to pick up other signals that are more generally associated with an output label. KnowMAN strongly improves results compared to direct weakly supervised learning with a pre-trained transformer language model and a feature-based baseline.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
