Improving Persian Relation Extraction Models by Data Augmentation
This work addresses the problem of limited data for Persian relation extraction, which is incremental as it builds on existing datasets and models.
The paper tackled the lack of comprehensive datasets for Persian relation extraction by augmenting the PERLEX dataset with text preprocessing and data augmentation techniques, resulting in a best model achieving 64.67% Macro-F1 on a contest test set and 83.68% on the PERLEX test set.
Relation extraction that is the task of predicting semantic relation type between entities in a sentence or document is an important task in natural language processing. Although there are many researches and datasets for English, Persian suffers from sufficient researches and comprehensive datasets. The only available Persian dataset for this task is PERLEX, which is a Persian expert-translated version of the SemEval-2010-Task-8 dataset. In this paper, we present our augmented dataset and the results and findings of our system, participated in the Persian relation Extraction shared task of NSURL 2021 workshop. We use PERLEX as the base dataset and enhance it by applying some text preprocessing steps and by increasing its size via data augmentation techniques to improve the generalization and robustness of applied models. We then employ two different models including ParsBERT and multilingual BERT for relation extraction on the augmented PERLEX dataset. Our best model obtained 64.67% of Macro-F1 on the test phase of the contest and it achieved 83.68% of Macro-F1 on the test set of PERLEX.