In low-resource natural language processing (NLP), the key problems are a
lack of target language training data, and a lack of native speakers to create
it. Cross-lingual methods have had notable success in addressing these
concerns, but in certain common circumstances, such as insufficient
pre-training corpora or languages far from the source language, their
performance suffers. In this work we propose a complementary approach to
building low-resource Named Entity Recognition (NER) models using
``non-speaker'' (NS) annotations, provided by annotators with no prior
experience in the target language. We recruit 30 participants in a carefully
controlled annotation experiment with Indonesian, Russian, and Hindi. We show
that use of NS annotators produces results that are consistently on par or
better than cross-lingual methods built on modern contextual representations,
and have the potential to outperform with additional effort. We conclude with
observations of common annotation patterns and recommended implementation
practices, and motivate how NS annotations can be used in addition to prior
methods for improved performance. For more details,
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通过使用非母语注释方法，在低资源情况下的自然语言处理中提出一种改进低资源 NER 模型的方法，通过实验验证该方法的有效性及优越性，可以在一定程度上替代现有的跨语言方法。

利用非说话者注释构建低资源命名实体识别模型

Building Low-Resource NER Models Using Non-Speaker Annotation

In the slot-filling paradigm, where a user can refer back to slots in the
context during the conversation, the goal of the contextual understanding
system is to resolve the referring expressions to the appropriate slots in the
context. In this paper, we build on the context carryover
system~\citep{Naik2018ContextualSC}, which provides a scalable multi-domain
framework for resolving references. However, scaling this approach across
languages is not a trivial task, due to the large demand on acquisition of
annotated data in the target language. Our main focus is on cross-lingual
methods for reference resolution as a way to alleviate the need for annotated
data in the target language. In the cross-lingual setup, we assume there is
access to annotated resources as well as a well trained model in the source
language and little to no annotated data in the target language. In this paper,
we explore three different approaches for cross-lingual transfer \textemdash~\
delexicalization as data augmentation, multilingual embeddings and machine
translation. We compare these approaches both on a low resource setting as well
as a large resource setting. Our experiments show that multilingual embeddings
and delexicalization via data augmentation have a significant impact in the low
resource setting, but the gains diminish as the amount of available data in the
target language increases. Furthermore, when combined with machine translation
we can get performance very close to actual live data in the target language,
with only 25\% of the data projected into the target language.

本文关注跨语言环境下的指代消解，探讨了三种不同的方法 -- 词汇去表示，多语种嵌入和机器翻译，并在低资源和大资源设置下进行了比较，发现在低资源的情况下，多语种嵌入和通过数据增强的词汇去表示方法具有显著的提升，但随着目标语言中可用数据量的增加，其增益逐渐减少。在结合机器翻译的情况下，我们可以获得接近目标语言实时数据的性能，仅需将 25％的数据投影到目标语言中。