Cross-Lingual Named Entity Recognition for Heterogenous Languages

Previous works on cross-lingual Named Entity Recognition (NER) have achieved great success. However, few of them consider the effect of language families between the source and target languages. In this study, we find that the cross-lingual NER performance of a target language would decrease when its source language is changed from the same (homogenous) into a different (heterogenous) language family with that target language. To improve the NER performance in this situation, we propose a novel cross-lingual NER framework based on self-distillation mechanism and Bilateral-Branch Network (SD-BBN). SD-BBN learns source-language NER knowledge from supervised datasets and obtains target-language knowledge from weakly supervised datasets. These two kinds of knowledge are then fused based on self-distillation mechanism for better identifying entities in the target language. We evaluate SD-BBN on 9 language datasets from 4 different language families. Results show that SD-BBN tends to outperform baseline methods. Remarkably, when the target and source languages are heterogenous, SD-BBN can achieve a greater boost. Our results might suggest that obtaining language-specific knowledge from the target language is essential for improving cross-lingual NER when the source and target languages are heterogenous. This finding could provide a novel insight into further research.