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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.
Recommendation Technique for Bug Fixers by Fine-tuning Language Models
Dae-Sung Wang, Hoon Seong, Chan-Gun Lee
http://doi.org/10.5626/JOK.2022.49.11.987
The scale and complexity of software continue to increase; hence they contribute to the occurrence of diverse bugs. Therefore, the necessity of systematic bug management has been raised. A few studies have proposed automating the assignment of bug fixers using word-based deep learning models. However, their accuracy is not satisfactory due to context of the word is ignored, and there is an excessive number of classes. In this paper, the accuracy was improved by about 27%p over the top-10 accuracies by using a fine-tuned pre-trained language model based on BERT, RoBERTa, DeBERTa, and CodeBERT. Experiments confirmed that the accuracy was about 70%. Through this, we showed that the fine-tuned pretrained language model could be effectively applied to automated bug-fixer assignments.
OANet: Ortho-Attention Net Based on Attention Mechanism for Database Performance Prediction
Chanho Yeom, Jieun Lee, Sanghyun Park
http://doi.org/10.5626/JOK.2022.49.11.1026
Various parameters in a database can be modified, which are called knobs. Since the performance of the database varies according to the settings of the knobs, it is important to tune the knobs of the database. And when tuning, a model that can reliably and quickly predict database performance according to the knob setting is needed. However, even when the knob setting is the same, the results may be different if the workload performing the benchmark is different. Therefore, in this paper, we propose an OANet using the attention mechanism so that the relationship between the knob and the workload can also be considered. Through experiments, the performance prediction results of the database were compared to various machine learning techniques, and the superiority of the model was confirmed by showing the highest score.
Korean-English Neural Machine Translation Using Korean Alphabet Characteristics and Honorific Expressions
Jeonghui Kim, Jaemu Heo, Joowhan Kim, Heeyoul Choi
http://doi.org/10.5626/JOK.2022.49.11.1017
Recently, deep learning has improved the performance of machine translation, but in most cases, it does not reflect the characteristics of the languages. In particular, Korean has unique linguistic word and expression features, which might cause mistranslation. For example, in Google Translate from Korean to English, mistranslations occur when a noun in Korean ends with the postposition (josa) in the form of a single consonant. Also, in the English-Korean translations, the honorifics and casual expressions are mixed in the translated results. This is because the alphabetic characteristics and honorifics of the Korean language are not reflected. In this paper, to address these problems, we propose to train a model with sub-words composed of units of letters (jamo) and unifying honorific and casual expressions in the corpus. The experimental results confirmed that the proposed method resolved the problems mentioned above, and had a similar or slightly higher BLEU score compared to the existing method and the corpus.
Performance Evaluation Technique of Learning Model Based on Feature Cluster in Sensing Data of Collaborative Robots
Jinse Kim, Subin Bea, Ye-Seul Park, Jung-Won Lee
http://doi.org/10.5626/JOK.2022.49.10.824
Recently, attempts have been made to apply an artificial intelligence model to PHM(Prognostics and Health Management) of collaborative robots, a representative equipment of smart factories. However, typical models are developed in a heuristic way without preprocessing or analysis of sensing data collected by operating test programs. Therefore, in this paper, we proposed a model performance evaluation method based on feature cluster concept which could analyze features of time series sensing data with cycles collected from cooperative robots. To demonstrate the effectiveness of the proposed method, we applied it to a program classification model, an internal component of the motion fault detection network, and identified characteristics of data that contributed to performance degradation, which has not been revealed by existing method. This results enabled a qualitative evaluation of the performance of the model and provided directions to improving model performance.
VNF Anomaly Detection Method based on Unsupervised Machine Learning
Seondong Heo, Seunghoon Jeong, Hosang Yun
http://doi.org/10.5626/JOK.2022.49.9.780
By applying virtualization technology to telecommunication networks, it is possible to reduce hardware dependencies and provide flexible control and management to the operators. In addition, since Capital Expenditure (CAPEX) and Operating Expenditure (OPEX) can be reduced by utilizing the technology, modern telco operators and service providers are using Software-Defined Networking(SDN) and Network Function Virtualization (NFV) technology to provide services more efficiently. As SDN and NFV are widely used, cyber attacks on Vitualized Network Functions (VNF) that degrade the quality of service or cause service denial are increasing. In this study, we propose a VNF anomaly detection method based on unsupervised machine learning techniques that models the steady states of VNFs and detects abnormal states caused by cyber attacks.
KorSciQA 2.0: Question Answering Dataset for Machine Reading Comprehension of Korean Papers in Science & Technology Domain
Hyesoo Kong, Hwamook Yoon, Mihwan Hyun, Hyejin Lee, Jaewook Seol
http://doi.org/10.5626/JOK.2022.49.9.686
Recently, the performance of the Machine Reading Comprehension(MRC) system has been increased through various open-ended Question Answering(QA) task, and challenging QA task which has to comprehensively understand multiple text paragraphs and make discrete inferences is being released to train more intelligent MRC systems. However, due to the absence of a QA dataset for complex reasoning to understand academic information in Korean, MRC research on academic papers has been limited. In this paper, we constructed a QA dataset, KorSciQA 2.0, for the full text including abstracts of Korean academic papers and divided the difficulty level into general, easy, and hard for discriminative MRC systems. A methodology, process, and system for constructing KorSciQA 2.0 were proposed. We conducted MRC performance evaluation experiments and when fine-tuning based on the KorSciBERT model, which is a Korean-based BERT model for science and technology domains, the F1 score was 80.76%, showing the highest performance.
Korean Dependency Parsing using Subtree Linking based on Machine Reading Comprehension
Jinwoo Min, Seung-Hoon Na, Jong-Hoon Shin, Young-Kil Kim, Kangil Kim
http://doi.org/10.5626/JOK.2022.49.8.617
In Korean dependency parsing, biaffine attention models have shown state-of-the-art performances; they first obtain head-level and modifier-level representations by applying two multi-layer perceptrons (MLP) on the encoded contextualized word representation, perform the attention by regarding modifier-level representation as a query and head-level one as a key, and take the resulting attention score as a probability of forming a dependency arc between the corresponding two words. However, given two target words (i.e., candidate head and modifier), biaffine attention methods are basically limited to their word-level representations, not being aware of the explicit boundaries of their phrases or subtrees. Thus, without relying on semantically and syntactically enriched phrase-level and subtree-level representations, biaffine attention methods might be not effective in the case that determining a dependency arc is not simple but complicated such as identifying a dependency between “far-distant” words, where these cases may often require subtree or phrase-level information surrounding target words. To address this drawback, this paper presents the use of dependency paring framework based on machine reading comprehension (MRC) that explicitly utilizes the subtree-level information by mapping a given child subtree and its parent subtree to a question and an answer, respectively. The experiment results on standard datasets of Korean dependency parsing shows that the MRC-based dependency paring outperforms the biaffine attention model. In particular, the results further given observations that improvements in performances are likely strong in long sentences, comparing to short ones.
A Cross Domain Adaptation Method based on Adversarial Cycle Consistence Learning for Rotary Machine Fault Diagnosis
http://doi.org/10.5626/JOK.2022.49.7.530
Research on data-based fault diagnosis models is being actively conducted in various industries. However, in the case of industrial equipment, various operating conditions occur, and it is difficult to secure sufficient training data. To solve this problem, a cross-domain adaptation technique can be utilized. In this study, we propose an adversarial consistency-maintaining transformation learning method that can maintain failure classification consistency even for the new untrained environmental data using the rotating body vibration data. The data generated through consistent learning creates a continuous invariant latent space between the new operating condition data distribution and the known data distribution and learns to maintain the failure classification performance through an adversarial learning network that shares the failure classification characteristic information. Therefore, the proposed method can provide a more stable and general classification performance by expanding the potential space to minimize the discrepancy between domain data. The experimental results of the proposed model showed about 88% accuracy for a real-machine dataset, and compared to the existing cross-domain adaptive learning methods, it showed a performance improvement of about 5-10%. According to the results of this study, it is expected to be an effective solution for the problem of equipment failure diagnosis at actual industrial sites.
Grammar Accuracy Evaluation (GAE): Quantifiable Qualitative Evaluation of Machine Translation Models
Dojun Park, Youngjin Jang, Harksoo Kim
http://doi.org/10.5626/JOK.2022.49.7.514
Natural Language Generation (NLG) refers to the operation of expressing the calculation results of a system in human language. Since the quality of generated sentences from an NLG model cannot be fully represented using only quantitative evaluation, they are evaluated using qualitative evaluation by humans in which the meaning or grammar of a sentence is scored according to a subjective criterion. Nevertheless, the existing evaluation methods have a problem as a large score deviation occurs depending on the criteria of evaluators. In this paper, we propose Grammar Accuracy Evaluation (GAE) that can provide the specific evaluating criteria. As a result of analyzing the quality of machine translation by BLEU and GAE, it was confirmed that the BLEU score does not represent the absolute performance of machine translation models and GAE compensates for the shortcomings of BLEU with flexible evaluation of alternative synonyms and changes in sentence structure.
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