Cross-domain NER is a challenging yet practical problem . We investigate a multi-cell compositional LSTM structure for multi-task learning . Theoretically, the resulting distinct feature distributions for each entity type make it more powerful for cross-domain transfer . Empirically, experiments on four few-shot and zero-shot datasets show our method significantly outperforms a series of multi- task learning methods and achieves the best results. Empirally, experiments show that the method significantly outshines multi-Task learning methods .

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Keywords : multi - domain - learning - task - shot -

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