  Named entity recognition (NER) is a fundamental component in many
applications, such as Web Search and Voice Assistants. Although deep neural
networks greatly improve the performance of NER, due to the requirement of
large amounts of training data, deep neural networks can hardly scale out to
many languages in an industry setting. To tackle this challenge, cross-lingual
NER transfers knowledge from a rich-resource language to languages with low
resources through pre-trained multilingual language models. Instead of using
training data in target languages, cross-lingual NER has to rely on only
training data in source languages, and optionally adds the translated training
data derived from source languages. However, the existing cross-lingual NER
methods do not make good use of rich unlabeled data in target languages, which
is relatively easy to collect in industry applications. To address the
opportunities and challenges, in this paper we describe our novel practice in
Microsoft to leverage such large amounts of unlabeled data in target languages
in real production settings. To effectively extract weak supervision signals
from the unlabeled data, we develop a novel approach based on the ideas of
semi-supervised learning and reinforcement learning. The empirical study on
three benchmark data sets verifies that our approach establishes the new
state-of-the-art performance with clear edges. Now, the NER techniques reported
in this paper are on their way to become a fundamental component for Web
ranking, Entity Pane, Answers Triggering, and Question Answering in the
Microsoft Bing search engine. Moreover, our techniques will also serve as part
of the Spoken Language Understanding module for a commercial voice assistant.
We plan to open source the code of the prototype framework after deployment.
