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The Yunshan Cup 2020 track focused on creating a framework for evaluatingdifferent 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 Laotagging. The Indonesian dataset is comprised of 10000 sentences from Indonesiannews within 29 tags. And the Lao dataset consists of 8000 sentences within 27tags. 25 teams registered for the task. The methods of participants ranged fromfeature-based to neural networks using either classical machine learningtechniques or ensemble methods. The best performing results achieve an accuracyof 95.82% for Indonesian and 93.03%, showing that neural sequence labelingmodels significantly outperform classic feature-based methods and rule-basedmethods.

Yunshan Cup 2020: Overview of the Part-of-Speech Tagging Task for Low-resourced Languages