Pretrained multilingual encoders enable zero-shot cross-lingual transfer, but
often produce unreliable models that exhibit high performance variance on the
target language. We postulate that this high variance results from zero-shot
cross-lingual transfer solving an under-specified optimization problem. We show
that any linear-interpolated model between the source language monolingual
model and source + target bilingual model has equally low source language
generalization error, yet the target language generalization error reduces
smoothly and linearly as we move from the monolingual to bilingual model,
suggesting that the model struggles to identify good solutions for both source
and target languages using the source language alone. Additionally, we show
that zero-shot solution lies in non-flat region of target language error
generalization surface, causing the high variance.

通过研究，我们发现使用预训练的多语言编码器进行零样本跨语言转移时，可能会产生高方差的不可靠模型，这是由于零样本跨语言转移解决了欠约束的优化问题，而线性插值模型可以同时对源语言和目标语言进行较好地建模，因此可以作为更好的方案。此外，零样本解决方案位于目标语言错误泛化表面的非平坦区域，导致了高方差。

零样本跨语言转移是未规范化的优化

Zero-shot Cross-lingual Transfer is Under-specified Optimization

Semantic parsing (SP) allows humans to leverage vast knowledge resources
through natural interaction. However, parsers are mostly designed for and
evaluated on English resources, such as CFQ (Keysers et al., 2020), the current
standard benchmark based on English data generated from grammar rules and
oriented towards Freebase, an outdated knowledge base. We propose a method for
creating a multilingual, parallel dataset of question-query pairs, grounded in
Wikidata. We introduce such a dataset, which we call Multilingual Compositional
Wikidata Questions (MCWQ), and use it to analyze the compositional
generalization of semantic parsers in Hebrew, Kannada, Chinese and English.
While within-language generalization is comparable across languages,
experiments on zero-shot cross-lingual transfer demonstrate that cross-lingual
compositional generalization fails, even with state-of-the-art pretrained
multilingual encoders. Furthermore, our methodology, dataset and results will
facilitate future research on SP in more realistic and diverse settings than
has been possible with existing resources.

本篇文章提出了一种基于 Wikidata 的多语言、平行的问句对数据集，即 Multilingual Compositional Wikidata Questions（MCWQ），用于分析语义解析器在英语、希伯来语、卡纳达语和汉语中的组合泛化能力。结果表明，即使使用最先进的预训练多语言编码器，跨语言组合泛化能力也无法实现，文章的方法、数据集和结果将有助于未来关于具有更现实和多样化背景下语义解析的研究。