Look Ma, Only 400 Samples! Revisiting the Effectiveness of Automatic N-Gram Rule Generation for Spelling Normalization in Filipino

Lorenzo Jaime Flores, Dragomir Radev


Abstract
With 84.75 million Filipinos online, the ability for models to process online text is crucial for developing Filipino NLP applications. To this end, spelling correction is a crucial preprocessing step for downstream processing. However, the lack of data prevents the use of language models for this task. In this paper, we propose an N-Gram + Damerau-Levenshtein distance model with automatic rule extraction. We train the model on 300 samples, and show that despite limited training data, it achieves good performance and outperforms other deep learning approaches in terms of accuracy and edit distance. Moreover, the model (1) requires little compute power, (2) trains in little time, thus allowing for retraining, and (3) is easily interpretable, allowing for direct troubleshooting, highlighting the success of traditional approaches over more complex deep learning models in settings where data is unavailable.
Anthology ID:
2022.sustainlp-1.5
Volume:
Proceedings of The Third Workshop on Simple and Efficient Natural Language Processing (SustaiNLP)
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates (Hybrid)
Venue:
sustainlp
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
29–35
Language:
URL:
https://aclanthology.org/2022.sustainlp-1.5
DOI:
Bibkey:
Cite (ACL):
Lorenzo Jaime Flores and Dragomir Radev. 2022. Look Ma, Only 400 Samples! Revisiting the Effectiveness of Automatic N-Gram Rule Generation for Spelling Normalization in Filipino. In Proceedings of The Third Workshop on Simple and Efficient Natural Language Processing (SustaiNLP), pages 29–35, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.
Cite (Informal):
Look Ma, Only 400 Samples! Revisiting the Effectiveness of Automatic N-Gram Rule Generation for Spelling Normalization in Filipino (Flores & Radev, sustainlp 2022)
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PDF:
https://preview.aclanthology.org/ingestion-script-update/2022.sustainlp-1.5.pdf