Cross-lingual word embeddings transfer knowledge between languages: models
trained on high-resource languages can predict in low-resource languages. We
introduce CLIME, an interactive system to quickly refine cross-lingual word
embeddings for a given classification problem. First, CLIME ranks words by
their salience to the downstream task. Then, users mark similarity between
keywords and their nearest neighbors in the embedding space. Finally, CLIME
updates the embeddings using the annotations. We evaluate CLIME on identifying
health-related text in four low-resource languages: Ilocano, Sinhalese,
Tigrinya, and Uyghur. Embeddings refined by CLIME capture more nuanced word
semantics and have higher test accuracy than the original embeddings. CLIME
often improves accuracy faster than an active learning baseline and can be
easily combined with active learning to improve results.

CLIME 是一个交互式系统，用于对具有分类问题的跨语言单词嵌入进行快速精化，使之能够适应低资源语言。通过对四种低资源语言进行健康相关文本识别，使用 CLIME 可以捕获更微妙的单词语义，并具有更高的测试准确性，而且 CLIME 经常比主动学习基线更快地提高准确性，并且易于与主动学习结合使用以改进结果。

跨语言词嵌入的交互式优化

Interactive Refinement of Cross-Lingual Word Embeddings

Various $\ell_1$-penalised estimation methods such as graphical lasso and
CLIME are widely used for sparse precision matrix estimation. Many of these
methods have been shown to be consistent under various quantitative assumptions
about the underlying true covariance matrix. Intuitively, these conditions are
related to situations where the penalty term will dominate the optimisation. In
this paper, we explore the consistency of $\ell_1$-based methods for a class of
sparse latent variable -like models, which are strongly motivated by several
types of applications. We show that all $\ell_1$-based methods fail
dramatically for models with nearly linear dependencies between the variables.
We also study the consistency on models derived from real gene expression data
and note that the assumptions needed for consistency never hold even for modest
sized gene networks and $\ell_1$-based methods also become unreliable in
practice for larger networks.

本文研究了基于 L1 范数的估计方法在一类带稀疏潜变量模型中的一致性问题，证明了存在变量近似线性相关的情况下这些方法会失效，而且 L1 方法在实际的基因网络中也不可靠。