  Zero-resource cross-lingual transfer approaches aim to apply supervised
models from a source language to unlabelled target languages. In this paper we
perform an in-depth study of the two main techniques employed so far for
cross-lingual zero-resource sequence labelling, based either on data or model
transfer. Although previous research has proposed translation and annotation
projection (data-based cross-lingual transfer) as an effective technique for
cross-lingual sequence labelling, in this paper we experimentally demonstrate
that high capacity multilingual language models applied in a zero-shot
(model-based cross-lingual transfer) setting consistently outperform data-based
cross-lingual transfer approaches. A detailed analysis of our results suggests
that this might be due to important differences in language use. More
specifically, machine translation often generates a textual signal which is
different to what the models are exposed to when using gold standard data,
which affects both the fine-tuning and evaluation processes. Our results also
indicate that data-based cross-lingual transfer approaches remain a competitive
option when high-capacity multilingual language models are not available.
