Zero-Resource Cross-Domain Named Entity Recognition
Table 1: F1-scores on the target domain. Models are implemented based on the corresponding embeddings.
['[BOLD] Model', '[BOLD] BERT  [BOLD] Fine-tune', '[BOLD] FastText  [BOLD] unfreeze', '[BOLD] FastText  [BOLD] freeze']
We conjecture that these two baselines, which utilize slot descriptions or slot examples, are suitable for limited slot names in the slot filling task, while they fail to cope with wide variances of entity names in the NER task across different domains, while our model is more robust to the domain variations. MTL helps our model recognize named entities in the target domain, while the MoEE adds information from different entity experts and helps our model detect the specific named entity types. Surprisingly, the performance of our best model (with freezed FastText embeddings) is close to that of the state-of-the-art model that needs a large data corpus in the source and target domains, which illustrates our model’s generalization ability to the target domain.