Fine-Tuned IndoBERT based model and data augmentation for indonesian language paraphrase identification

Abstract

Natural Language Processing tasks in the Indonesian language have recently flourished thanks to the research of IndoBERT and its benchmark. Despite being the fourth most used language over the internet, the Indonesian language NLP task still has some gaps, one of them being the Paraphrase Identification task. In order to solve this gap, we proposed a fine-Tuned IndoBERT based model for Paraphrase Identification. Several methods have been researched in this paper from setting the baseline, Data Augmentation, fine-Tune the classifier, and task reformulation. Besides the model, this paper also provides the Paraphrase Identification dataset in Indonesian language. The baseline IndoBERT model performs well, it proves that IndoBERT is one of the fittest methods to use. We then researched further and proposed a Modified Easy Data Augmentation that augments very well in this task and potentially on other NLP tasks. We compared traditional machine learning classifiers with deep neural network classifiers, fine-Tuned them, and selected the best classifier for this task. Furthermore, we tried entailment task reformulation. The Modified EDA shows a successful augmentation that increases both accuracy and F1 score for all the models. A slightly complex upgrade for the classifier also increased the performance while maintaining a reasonable training time.