De-biasing Distantly Supervised Named Entity Recognition via Causal Intervention

Understand

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.