Massively Multilingual Transfer for NER
Table 2: The performance of RaRe and BEA in terms of phrase-based F1 on CoNLL NER datasets compared with state-of-the-art benchmark methods. Resource requirements are indicated with superscripts, p: parallel corpus, w: Wikipedia, d: dictionary, l: 100 NER annotation, 0: no extra resources.
['lang.', 'de', 'es', 'nl', 'en']
S4SS0SSS0Px4 CoNLL Dataset Finally, we apply our model to the CoNLL-02/03 datasets, to benchmark our technique against related work. This corpus is much less rich than Wikiann used above, as it includes only four languages (en, de, nl, es), and furthermore, the languages are closely related and share the same script. Note that there are only 3 source models and none of them is clearly bad , so BEA estimates that they are similarly reliable which results in little difference in terms of performance between BEA and MV.