5 papers with code • 1 benchmarks • 1 datasets
In this paper, we propose a Coarse-to-fine approach (Coach) for cross-domain slot filling.
Cross-domain named entity recognition (NER) models are able to cope with the scarcity issue of NER samples in target domains.
Recent research efforts have shown that neural architectures can be effective in conventional information extraction tasks such as named entity recognition, yielding state-of-the-art results on standard newswire datasets.
Existing models for cross-domain named entity recognition (NER) rely on numerous unlabeled corpus or labeled NER training data in target domains.
Ranked #1 on Cross-Domain Named Entity Recognition on CoNLL04