We consider a multilingual weakly supervised learning scenario where
knowledge from annotated corpora in a resource-rich language is transferred via
bitext to guide the learning in other languages. Past approaches project labels
across bitext and use them as features or gold labels for training. We propose
a new method that projects model expectations rather than labels, which
facilities transfer of model uncertainty across language boundaries. We encode
expectations as constraints and train a discriminative CRF model using
Generalized Expectation Criteria (Mann and McCallum, 2010). Evaluated on
standard Chinese-English and German-English NER datasets, our method
demonstrates F1 scores of 64% and 60% when no labeled data is used. Attaining
the same accuracy with supervised CRFs requires 12k and 1.5k labeled sentences.
Furthermore, when combined with labeled examples, our method yields significant
improvements over state-of-the-art supervised methods, achieving best reported
numbers to date on Chinese OntoNotes and German CoNLL-03 datasets.

本文提出了基于 bitext 资源将多语种低监督学习领域的已标注语料库知识转移的一种新方法，该方法使用推断的模型期望而不是标签来指导学习。在标注数据不足的情况下，评估结果表明，在标准的中英和德英 NER 数据集上，我们的方法表现出 64％和 60％的 F1 得分。与监督 CRFs 相比，实现相同准确性需要 12k 和 1.5k 标记句子。此外，当与标记示例结合使用时，我们的方法在中文 OntoNotes 和德语 CoNLL-03 数据集上获得了迄今为止报告的最佳结果。