Distant supervision tackles the data bottleneck in NER by automatically
generating training instances via dictionary matching. Unfortunately, the
learning of DS-NER is severely dictionary-biased, which suffers from spurious
correlations and therefore undermines the effectiveness and the robustness of
the learned models. In this paper, we fundamentally explain the dictionary bias
via a Structural Causal Model (SCM), categorize the bias into intra-dictionary
and inter-dictionary biases, and identify their causes. Based on the SCM, we
learn de-biased DS-NER via causal interventions. For intra-dictionary bias, we
conduct backdoor adjustment to remove the spurious correlations introduced by
the dictionary confounder. For inter-dictionary bias, we propose a causal
invariance regularizer which will make DS-NER models more robust to the
perturbation of dictionaries. Experiments on four datasets and three DS-NER
models show that our method can significantly improve the performance of
DS-NER.

该论文通过结构因果模型对 DS-NER 中的词典偏差进行解释和分类，并提出了一种基于因果干预的去除偏差的方法，包括通过后门调整和因果不变正则化器来提高模型鲁棒性，实验证明该方法显著提高了 DS-NER 的性能。