In low-resource settings, the performance of supervised labeling models can
be improved with automatically annotated or distantly supervised data, which is
cheap to create but often noisy. Previous works have shown that significant
improvements can be reached by injecting information about the confusion
between clean and noisy labels in this additional training data into the
classifier training. However, for noise estimation, these approaches either do
not take the input features (in our case word embeddings) into account, or they
need to learn the noise modeling from scratch which can be difficult in a
low-resource setting. We propose to cluster the training data using the input
features and then compute different confusion matrices for each cluster. To the
best of our knowledge, our approach is the first to leverage feature-dependent
noise modeling with pre-initialized confusion matrices. We evaluate on
low-resource named entity recognition settings in several languages, showing
that our methods improve upon other confusion-matrix based methods by up to 9%.

在资源匮乏的情况下，使用自动注释或远程监督数据可以改善监督标注模型的性能，但这些数据往往信息有噪声，并且噪声估计需要考虑输入特征和预初始化的混淆矩阵。我们提出将训练数据使用输入特征进行聚类，为每个簇计算不同的混淆矩阵，并在多种语言的命名实体识别场景中进行评估，显示我们的方法比其他混淆矩阵方法提高了高达 9% 的性能。