What Matters for Neural Cross-Lingual Named Entity Recognition: An Empirical Analysis
Table 4: F1 scores of different lengths of entities across the three languages: Spanish, Dutch and German. The number 1 refers to the entities with single token, the 2 means the entities with two tokens and the ≥ 3 indicates the entities with not less than three tokens.
['Language', '1', '2', '≥3']
While we can observe how models perform overall on the named entities, we can not know how models differ from short to long entities. Particularly, probing if models hold strong biases towards shorter entities can help interpret the process of cross-lingual transfer learning. To make the comparison, we first categorize entities into three levels: single token, two tokens and greater or equal to three tokens. We then calculate the F1-score of the three entity lengths in the test set and among correctly predicted entities.