Despite the impressive growth of the abilities of multilingual language
models, such as XLM-R and mT5, it has been shown that they still face
difficulties when tackling typologically-distant languages, particularly in the
low-resource setting. One obstacle for effective cross-lingual transfer is
variability in word-order patterns. It can be potentially mitigated via source-
or target-side word reordering, and numerous approaches to reordering have been
proposed. However, they rely on language-specific rules, work on the level of
POS tags, or only target the main clause, leaving subordinate clauses intact.
To address these limitations, we present a new powerful reordering method,
defined in terms of Universal Dependencies, that is able to learn fine-grained
word-order patterns conditioned on the syntactic context from a small amount of
annotated data and can be applied at all levels of the syntactic tree. We
conduct experiments on a diverse set of tasks and show that our method
consistently outperforms strong baselines over different language pairs and
model architectures. This performance advantage holds true in both zero-shot
and few-shot scenarios.

使用依赖通用模式的强大重排序方法，能从少量的标注数据中学习到依赖语法上下文的精细词序模式，并在所有语法树的层级上应用，通过实验验证在零样本和少样本场景下相比强基准模型始终有优势。