2 research outputs found
Look Ma, Only 400 Samples! Revisiting the Effectiveness of Automatic N-Gram Rule Generation for Spelling Normalization in Filipino
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.Comment: 4 pages, 1 figure, Presented at EMNLP 2022 Third Workshop on Simple
and Efficient Natural Language Processin