  Recent research on sequence labelling has been exploring different strategies
to mitigate the lack of manually annotated data for the large majority of the
world languages. Among others, the most successful approaches have been based
on (i) the cross-lingual transfer capabilities of multilingual pre-trained
language models (model-transfer), (ii) data translation and label projection
(data-transfer) and (iii), prompt-based learning by reusing the mask objective
to exploit the few-shot capabilities of pre-trained language models (few-shot).
Previous work seems to conclude that model-transfer outperforms data-transfer
methods and that few-shot techniques based on prompting are superior to
updating the model's weights via fine-tuning. In this paper, we empirically
demonstrate that, for Argument Mining, a sequence labelling task which requires
the detection of long and complex discourse structures, previous insights on
cross-lingual transfer or few-shot learning do not apply. Contrary to previous
work, we show that for Argument Mining data transfer obtains better results
than model-transfer and that fine-tuning outperforms few-shot methods.
Regarding the former, the domain of the dataset used for data-transfer seems to
be a deciding factor, while, for few-shot, the type of task (length and
complexity of the sequence spans) and sampling method prove to be crucial.
