Neural Cross-Lingual Named Entity Recognition with Minimal Resources
Table 1: NER F1 scores. ∗Approaches that use more resources than ours (“Wikipedia” means Wikipedia is used not as a monolingual corpus, but to provide external knowledge). †Approaches that use multiple languages for transfer. “Only Eng. data” is the model used in Mayhew et al. (2017) trained on their data translated from English without using Wikipedia and other languages. The “data from Mayhew et al. (2017)” is the same data translated from only English they used. “Id.c.” indicates using identical character strings between the two languages as the seed dictionary. “Adv.” indicates using adversarial training and mutual nearest neighbors to induce a seed dictionary. Our supervised results are obtained using models trained on annotated corpus from CoNLL.
['Model ∗', 'Model Täckström et\xa0al. ( 2012 )', 'Spanish 59.30', 'Dutch 58.40', 'German 40.40', 'Extra Resources parallel corpus']
Here “BWET” (bilingual word embedding translation) denotes using the hierarchical neural CRF model trained on data translated from English. As can be seen from the table, our methods outperform previous state-of-the-art results on Spanish and Dutch by a large margin and perform competitively on German even without using any parallel resources. We achieve similar results using different seed dictionaries, and produce the best results when adding the self-attention mechanism to our model.