  In recent years, great success has been achieved in many tasks of natural
language processing (NLP), e.g., named entity recognition (NER), especially in
the high-resource language, i.e., English, thanks in part to the considerable
amount of labeled resources. However, most low-resource languages do not have
such an abundance of labeled data as high-resource English, leading to poor
performance of NER in these low-resource languages. Inspired by knowledge
transfer, we propose Converse Attention Network, or CAN in short, to improve
the performance of NER in low-resource languages by leveraging the knowledge
learned in pretrained high-resource English models. CAN first translates
low-resource languages into high-resource English using an attention based
translation module. In the process of translation, CAN obtain the attention
matrices that align the two languages. Furthermore, CAN use the attention
matrices to align the high-resource semantic features from a pretrained
high-resource English model with the low-resource semantic features. As a
result, CAN obtains aligned high-resource semantic features to enrich the
representations of low-resource languages. Experiments on four low-resource NER
datasets show that CAN achieves consistent and significant performance
improvements, which indicates the effectiveness of CAN.
