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Building a Parallel Corpus and Training Translation Models Between Luganda and English
Richard Kimera, Daniela N. Rim, Heeyoul Choi
http://doi.org/10.5626/JOK.2022.49.11.1009
Recently, neural machine translation (NMT) which has achieved great successes needs large datasets, so NMT is more premised on high-resource languages. This continuously underpins the low resource languages such as Luganda due to the lack of high-quality parallel corpora, so even ‘Google translate’ does not serve Luganda at the time of this writing. In this paper, we build a parallel corpus with 41,070 pairwise sentences for Luganda and English which is based on three different open-sourced corpora. Then, we train NMT models with hyper-parameter search on the dataset. Experiments gave us a BLEU score of 21.28 from Luganda to English and 17.47 from English to Luganda. Some translation examples show high quality of the translation. We believe that our model is the first Luganda-English NMT model. The bilingual dataset we built will be available to the public.
Identification of Generative Adversarial Network Models Suitable for Software Defect Prediction
Jiwon Choi, Jaewook Lee, Duksan Ryu, Suntae Kim
http://doi.org/10.5626/JOK.2022.49.1.52
Software Defect Prediction(SDP) helps effectively allocate quality assurance resources which are limited by identifying modules that are likely to cause defects. Software defect data suffer from class imbalance problems in which there are more non-defective instances than defective instances. In most machine learning methods, the defect prediction performance is degraded when there is a disproportionate number of instances belonging to a particular class. Therefore, this research aimed to solve the class imbalance problem and improve defect prediction performance by using a Generative Adversarial Network(GAN) model. To this end, we compared different kinds of GAN models for their suitability for SDP and checked the applicability of GAN models that were not applied in the related work. In our study, Vanilla-GAN(GAN), Conditional GAN (cGAN), and Wasserstein GAN (WGAN) models which were initially proposed for image generation were adapted for software defect prediction. Then those modified models were compared with Tabular GAN(TGAN) and Modeling Tabular data using Conditional GAN(CTGAN). Our experimental results showed that the CTGAN model is suitable for SDP data. We also conducted a sensitivity analysis examining which hyper-parameter values of CTGAN increase the recall rate and lower the probability of false alarm (PF). Our experimental results indicated that the hyper-parameters should be adjusted according to the dataset. We expect that our proposed approach can help effectively allocate limited resources by improving the performance of SDP.
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