Multi-Cell Compositional LSTM for NER Domain Adaptation
Jia Chen, Yue Zhang
Abstract
Cross-domain NER is a challenging yet practical problem. Entity mentions can be highly different across domains. However, the correlations between entity types can be relatively more stable across domains. We investigate a multi-cell compositional LSTM structure for multi-task learning, modeling each entity type using a separate cell state. With the help of entity typed units, cross-domain knowledge transfer can be made in an entity type level. 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 zeroshot datasets show our method significantly outperforms a series of multi-task learning methods and achieves the best results.