  Named entity recognition (NER) is a fundamental task of natural language
processing (NLP). However, most state-of-the-art research is mainly oriented to
high-resource languages such as English and has not been widely applied to
low-resource languages. In Malay language, relevant NER resources are limited.
In this work, we propose a dataset construction framework, which is based on
labeled datasets of homologous languages and iterative optimization, to build a
Malay NER dataset (MYNER) comprising 28,991 sentences (over 384 thousand
tokens). Additionally, to better integrate boundary information for NER, we
propose a multi-task (MT) model with a bidirectional revision (Bi-revision)
mechanism for Malay NER task. Specifically, an auxiliary task, boundary
detection, is introduced to improve NER training in both explicit and implicit
ways. Furthermore, a gated ignoring mechanism is proposed to conduct
conditional label transfer and alleviate error propagation by the auxiliary
task. Experimental results demonstrate that our model achieves comparable
results over baselines on MYNER. The dataset and the model in this paper would
be publicly released as a benchmark dataset.
