Existing algorithms for aligning cross-lingual word vector spaces assume that
vector spaces are approximately isomorphic. As a result, they perform poorly or
fail completely on non-isomorphic spaces. Such non-isomorphism has been
hypothesised to result from typological differences between languages. In this
work, we ask whether non-isomorphism is also crucially a sign of degenerate
word vector spaces. We present a series of experiments across diverse languages
which show that variance in performance across language pairs is not only due
to typological differences, but can mostly be attributed to the size of the
monolingual resources available, and to the properties and duration of
monolingual training (e.g. "under-training").

研究跨语言字向量空间对齐的算法，证明非同构空间不一定是由于语言间的类型差异，而是受到单语资源的大小和单语训练的影响，尤其是训练不充分的影响。

所有良好的词向量空间都同构吗？

Are All Good Word Vector Spaces Isomorphic?

In this paper, we propose efficient and less resource-intensive strategies
for parsing of code-mixed data. These strategies are not constrained by
in-domain annotations, rather they leverage pre-existing monolingual annotated
resources for training. We show that these methods can produce significantly
better results as compared to an informed baseline. Besides, we also present a
data set of 450 Hindi and English code-mixed tweets of Hindi multilingual
speakers for evaluation. The data set is manually annotated with Universal
Dependencies.

本文提出了一种有效且资源占用较少的策略来解析混合编码数据，利用现有的单一语言标注资源进行训练，相比专业领域的方法，这些方法可以取得显着更好的结果。同时，我们还提供了一个包含 450 个印地文和英文编码混合推文数据集及其通用依存分析标注结果，用于评估这些方法。