Cross-Lingual Transfer for Distantly Supervised and Low-resources Indonesian NER

TL;DR

This paper explores fine-tuning pre-trained language models for cross-lingual transfer to improve Indonesian NER in low-resource settings, demonstrating significant gains especially with small gold datasets.

Contribution

It introduces a method of cross-lingual transfer using pre-trained language models, outperforming mono-lingual and POS-based transfer in low-resource NER tasks.

Findings

Significant improvement in small gold corpus scenarios.

Competitive results in large silver datasets.

Effective use of character-level input in bi-directional language models.

Abstract

Manually annotated corpora for low-resource languages are usually small in quantity (gold), or large but distantly supervised (silver). Inspired by recent progress of injecting pre-trained language model (LM) on many Natural Language Processing (NLP) task, we proposed to fine-tune pre-trained language model from high-resources languages to low-resources languages to improve the performance of both scenarios. Our empirical experiment demonstrates significant improvement when fine-tuning pre-trained language model in cross-lingual transfer scenarios for small gold corpus and competitive results in large silver compare to supervised cross-lingual transfer, which will be useful when there is no parallel annotation in the same task to begin. We compare our proposed method of cross-lingual transfer using pre-trained LM to different sources of transfer such as mono-lingual LM and Part-of-Speech tagging (POS) in the downstream task of both large silver and small gold NER dataset by exploiting character-level input of bi-directional language model task.