  Translating training data into many languages has emerged as a practical
solution for improving cross-lingual transfer. For tasks that involve
span-level annotations, such as information extraction or question answering,
an additional label projection step is required to map annotated spans onto the
translated texts. Recently, a few efforts have utilized a simple
mark-then-translate method to jointly perform translation and projection by
inserting special markers around the labeled spans in the original sentence.
However, as far as we are aware, no empirical analysis has been conducted on
how this approach compares to traditional annotation projection based on word
alignment. In this paper, we present an extensive empirical study across 57
languages and three tasks (QA, NER, and Event Extraction) to evaluate the
effectiveness and limitations of both methods, filling an important gap in the
literature. Experimental results show that our optimized version of
mark-then-translate, which we call EasyProject, is easily applied to many
languages and works surprisingly well, outperforming the more complex word
alignment-based methods. We analyze several key factors that affect the
end-task performance, and show EasyProject works well because it can accurately
preserve label span boundaries after translation. We will publicly release all
our code and data.
