An Open-Source Dataset and A Multi-Task Model for Malay Named Entity Recognition

• URL: http://arxiv.org/abs/2109.01293v1
• Date: Fri, 3 Sep 2021 03:29:25 GMT
• Title: An Open-Source Dataset and A Multi-Task Model for Malay Named Entity Recognition
• Authors: Yingwen Fu and Nankai Lin and Zhihe Yang and Shengyi Jiang
• Abstract summary: We build a Malay NER dataset (MYNER) comprising 28,991 sentences (over 384 thousand tokens) An auxiliary task, boundary detection, is introduced to improve NER training in both explicit and implicit ways.
• Score: 3.511753382329252
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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