Yunshan Cup 2020: Overview of the Part-of-Speech Tagging Task for Low-resourced Languages
This work addresses part-of-speech tagging for low-resourced languages like Indonesian and Lao, but it is incremental as it focuses on benchmarking existing methods.
The paper tackled part-of-speech tagging for low-resourced languages by evaluating methods on Indonesian and Lao datasets, with the best results achieving 95.82% accuracy for Indonesian and 93.03% for Lao.
The Yunshan Cup 2020 track focused on creating a framework for evaluating different methods of part-of-speech (POS). There were two tasks for this track: (1) POS tagging for the Indonesian language, and (2) POS tagging for the Lao tagging. The Indonesian dataset is comprised of 10000 sentences from Indonesian news within 29 tags. And the Lao dataset consists of 8000 sentences within 27 tags. 25 teams registered for the task. The methods of participants ranged from feature-based to neural networks using either classical machine learning techniques or ensemble methods. The best performing results achieve an accuracy of 95.82% for Indonesian and 93.03%, showing that neural sequence labeling models significantly outperform classic feature-based methods and rule-based methods.