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A VQG Framework for Accurate and Diverse Question Generation
http://doi.org/10.5626/JOK.2025.52.1.62
Visual Question Generation (VQG) aims to generate questions based on a given image, often utilizing additional information such as answers or answer types if necessary. A VQG system should be able to generate diverse questions for a single image, while maintaining relevance to the image alongside its additional information. However, models that highly focus on relevance to the image might overfit to the dataset, leading to limited diversity, while those that emphasize diversity might generate questions less related to the input. Therefore, balancing these two aspects is crucial in VQG. To address this challenge, we proposed BCVQG (BLIP-CVAE VQG), a system that could integrate a pre-trained vision-language model with a Conditional Variational AutoEncoder (CVAE). The effectiveness of the proposed method was validated through quantitative and qualitative evaluations on the VQA2.0 dataset.
Training Data Augmentation Technique for Machine Comprehension by Question-Answer Pairs Generation Models based on a Pretrained Encoder-Decoder Model
http://doi.org/10.5626/JOK.2022.49.2.166
The goal of Machine Reading Comprehension (MRC) research is to find answers to questions in documents. MRC research requires large-scale, high-quality data. However, individual researchers or small research institutes have limitations in constructing them. To overcome the limitations, in this paper, we propose an MRC data augmentation technique using a pre-training language model. This MRC data augmentation technique consists of a Q&A pair generation model and a data validation model. The Q&A pair generation model consists of an answer extraction model and a question generation model. Both models are constructed by fine-tuning the BART model. The data validation model is added to increase the reliability of the augmented data. It is used to verify the generated augmented data. The validation model is used by fine-tuning the ELECTRA model as an MRC model. To see the performance improvement of the MRC model through the data augmentation technique, we applied the data augmentation technique to KorQuAD v1.0 data. As a result of the experiment, compared to the previous model, the Exact Match(EM) Score increased up to 7.2 and the F1 Score increased up to 5.7.
A Span Matrix-based Answer Candidates Detection Model used 2-Step Learning
Boeun Kim, Youngjin Jang, Harksoo Kim
http://doi.org/10.5626/JOK.2021.48.5.539
Automatic data construction refers to a technology that automatically constructs data through algorithms or deep neural networks. The automated construction system of question-answer data aimed at in this paper was mainly studied through a question generation model, which signifies a model that generates questions related to a given paragraph. Previously, paragraph and answer candidates were entered into the question generation model and related questions were generated. The answer candidates" input to the question generation model was detected through a rule-based method or a method using a deep neural network. We judged that answer detection, which is a subtask of question generation, will have a great influence on question generation. Consequently, we have proposed answer candidates detection model and 2-step learning method using Span Matrix. An experiment was conducted to find out how the questions generated through various methods of extracting answer candidates affect the question-answering system. The proposed model extracted a large number of correct answers compared to the existing model, and the noise in the learning process was supplemented by using the entity name dataset. Apparently, it was confirmed that the question-answer data generated as answer candidates extracted by the proposed model contributed the most to the performance of the question-answer system.
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