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Deep Neural Network-Based Automated Essay Trait Scoring Model Incorporating Argument Structure Information
http://doi.org/10.5626/JOK.2023.50.8.662
Automated essay scoring is the task of having a model read a given essay and evaluate it automatically. This paper presents a method for automated essay scoring by creating essay representations that reflect argument structure of the essay using Argument Mining, and learning essay representations for each trait score. Results of our experiments indicated that the proposed essay representation outperformed representations obtained from pre-trained language models. Furthermore, it was found that learning different representations for each evaluation criterion was more effective for essay evaluation. The performance of the proposed model, as measured by the Quadratic Weighted Kappa (QWK) metric, improved from 0.543 to 0.627, showing a high level of agreement with human evaluations. Qualitative evaluations also showed that the proposed model demonstrated similar evaluation tendencies to human evaluations.
Optimizing Homomorphic Compiler HedgeHog for DNN Model based on CKKS Homomorphic Encryption Scheme
Dongkwon Lee, Gyejin Lee, Suchan Kim, Woosung Song, Dohyung Lee, Hoon Kim, Seunghan Jo, Kyuyeon Park, Kwangkeun Yi
http://doi.org/10.5626/JOK.2022.49.9.743
We present a new state-of-the-art optimizing homomorphic compiler HedgeHog based on high-level input language. Although homomorphic encryption enables safe and secure third party computation, it is hard to build high-performance HE applications without expertise. Homomorphic compiler lowers this hurdle, but most of the existing compilers are based on HE scheme that does not support real number computation and a few compilers based on the CKKS HE scheme that supports real number computation uses low-level input language. We present an optimizing compiler HedgeHog whose input language supports high-level DNN operators. In addition to its ease of use, compiled HE code shows a maximum of 22% more of speedup than the existing state-of-the-art compiler.
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.
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