While various avenues of research have been explored for iterative pruning,
little is known what effect pruning has on zero-shot test performance and its
potential implications on the choice of pruning criteria. This pruning setup is
particularly important for cross-lingual models that implicitly learn alignment
between language representations during pretraining, which if distorted via
pruning, not only leads to poorer performance on language data used for
retraining but also on zero-shot languages that are evaluated.
In this work, we show that there is a clear performance discrepancy in
magnitude-based pruning when comparing standard supervised learning to the
zero-shot setting. From this finding, we propose two weight regularizers that
aim to maximize the alignment between units of pruned and unpruned networks to
mitigate alignment distortion in pruned cross-lingual models and perform well
for both non zero-shot and zero-shot settings.
We provide experimental results on cross-lingual tasks for the zero-shot
setting using XLM-RoBERTa$_{\mathrm{Base}}$, where we also find that pruning
has varying degrees of representational degradation depending on the language
corresponding to the zero-shot test set. This is also the first study that
focuses on cross-lingual language model compression.

本论文研究了迭代剪枝在零样本测试性能上的影响，特别是对跨语言模型的影响，提出了两种权重正则化方法以缓解剪枝引起的跨语言性畸变，并实现了良好的性能表现。同时，这也是关注跨语言语言模型压缩的首个研究。