UniTrans : Unifying Model Transfer and Data Transfer for Cross-Lingual Named Entity Recognition with Unlabeled Data
Table 2: Results of the proposed UniTrans and prior state-of-the-art methods for zero-resource cross-lingual NER. † denotes the reported results w.r.t. freezing the bottom 3 layers of BERT as in this paper. We also report the standard deviation for reimplemented baselines and the proposed UniTrans (i.e., numbers in parentheses).
['[EMPTY]', 'es', 'nl', 'de', 'no', 'Average']
ranging from 1.66 for German [de] to 2.56 for Spanish [es]. Moreover, UniTrans achieves an average improvement of 1.63 F1-score over Data Transfer, and 2.60 F1-score over Model Transfer. All these results well demonstrate the effectiveness of the proposed UniTrans, which is mainly attributed to unifying model transfer with data transfer and leveraging unlabeled target-language data in UniTrans.