@article{Lyu2024, 
author = {Shengfei Lyu and Linghao Sun and Huixiong Yi and Yong Liu and Huanhuan Chen and Chunyan Miao},
title = {Converse Attention Knowledge Transfer for Low-Resource Named Entity Recognition},
year = {2024},
journal = {International Journal of Crowd Science},
volume = {8},
number = {3},
pages = {140-148},
keywords = {named entity recognition (NER), low-resource NER, converse attention network, knowledge transfer, transfer learning},
url = {https://www.sciopen.com/article/10.26599/IJCS.2023.9100014},
doi = {10.26599/IJCS.2023.9100014},
abstract = {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. More labeled resources, better word representations. 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 due to poor word representations. In the paper, we propose converse attention network (CAN) to augment word representations in low-resource languages from the high-resource language, improving the performance of NER in low-resource languages by transferring knowledge learned in the high-resource language. CAN first translates sentences in low-resource languages into high-resource English using an attention-based translation module. In the process of translation, CAN obtains the attention matrices that align word representations of high-resource language space and low-resource language space. Furthermore, CAN augments word representations learned in low-resource language space with word representations learned in high-resource language space using the attention matrices. Experiments on four low-resource NER datasets show that CAN achieves consistent and significant performance improvements, which indicates the effectiveness of CAN.}
}