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Recent studies have attempted to understand natural language and infer answers. Machine reading comprehension is one of the representatives, and several related datasets have been opened. However, there are few official open datasets for the Test of English for International Communication (TOEIC), which is widely used for evaluating people's English proficiency, and research for further advancement is not being actively conducted. We consider that the reason why deep learning research for TOEIC is difficult is due to the data scarcity problem, so we therefore propose two data augmentation methods to improve the model in a low resource environment. Considering the attributes of the semantic and grammar problem type in TOEIC, the proposed methods can augment the data similar to the real TOEIC problem by using POS-tagging and Lemmatizing. In addition, we confirmed the importance of understanding semantics and grammar in TOEIC through experiments on each proposed methodology and experiments according to the amount of data. The proposed methods address the data shortage problem of TOEIC and enable an acceptable human-level performance. [ABSTRACT FROM AUTHOR]

Return on advertising spend (ROAS) refers to the ratio of revenue generated by advertising projects to its expense. It is used to assess the effectiveness of advertising marketing. Several simulation-based controlled experiments, such as geo experiments, have been proposed recently. This refers to calculating ROAS by dividing a geographic region into a control group and a treatment group and comparing the ROAS generated in each group. However, the data collected through these experiments can only be used to analyze previously constructed data, making it difficult to use in an inductive process that predicts future profits or costs. Furthermore, to obtain ROAS for each advertising group, data must be collected under a new experimental setting each time, suggesting that there is a limitation in using previously collected data. Considering these, we present a method for predicting ROAS that does not require controlled experiments in data acquisition and validates its effectiveness through comparative experiments. Specifically, we propose a task deposition method that divides the end-to-end prediction task into the two-stage process: occurrence prediction and occurred ROAS regression. Through comparative experiments, we reveal that these approaches can effectively deal with the advertising data, in which the label is mainly set to zero-label. [ABSTRACT FROM AUTHOR]

The term "Frequently asked questions" (FAQ) refers to a query that is asked repeatedly and produces a manually constructed response. It is one of the most important factors influencing customer repurchase and brand loyalty; thus, most industry domains invest heavily in it. This has led to deep-learning-based retrieval models being studied. However, training a model and creating a database specializing in each industry domain comes at a high cost, especially when using a chatbot-based conversation system, as a large amount of resources must be continuously input for the FAQ system's maintenance. It is also difficult for small- and medium-sized companies and national institutions to build individualized training data and databases and obtain satisfactory results. As a result, based on the deep learning information retrieval module, we propose a method of returning responses to customer inquiries using only data that can be easily obtained from companies. We hybridize dense embedding and sparse embedding in this work to make it more robust in professional terms, and we propose new functions to adjust the weight ratio and scale the results returned by the two modules. [ABSTRACT FROM AUTHOR]

In this work, we have developed a neural network (NN) model that can analyze enrichment from depleted (0.2%) to low enriched uranium (4.5%) when UO2 waste with very low radioactivity was contained in a 1-L Marinelli beaker, even when the measurement time is as short as 10 s using a low-resolution detector. The average count rate was about 3800 cps. Measurement of uranium enrichment is necessary for quantitative analysis of uranium radioactivity for disposal of uranium waste. Previously studied uranium enrichment methods (infinite thickness (IT) method, peak ratio (PR) method, and relative-efficiency (RE) method) are difficult to use for field measurement due to many limitations of the algorithms. Among existing methods, the RE method is accurate but requires a long measurement time; there is also a limitation in which a high-resolution detector is essential. In this work, we proposed a model to predict uranium enrichment using a low-resolution detector and an artificial NN model. Furthermore, we validated the results of the NN models using an explainable AI algorithm and principal component analysis (PCA). When the measurement time was less than 60 s, the existing method failed to analyze uranium enrichment, but the proposed model can predict enrichment of uranium within 5% of relative error when 5 g of uranium powder was mixed with various waste (ash, soil, and concrete). [ABSTRACT FROM AUTHOR]