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Prompt Tuning For Korean Aspect-Based Sentiment Analysis
Bong-Su Kim, Seung-Ho Choi, Si-hyun Park, Jun-Ho Wang, Ji-Yoon Kim, Hyun-Kyu Jeon, Jung-Hoon Jang
http://doi.org/10.5626/JOK.2024.51.12.1043
Aspect-based sentiment analysis examines how emotions in text relate to specific aspects, such as product characteristics or service features. This paper presents a comprehensive methodology for applying prompt tuning techniques to multi-task token labeling challenges using aspect-based sentiment analysis data. The methodology includes a pipeline for identifying emotion expression domains, which generalizes the token labeling problem into a sequence labeling problem. It also suggests selecting templates to classify separated sequences based on aspects and emotions, and expanding label words to align with the dataset’s characteristics, thus optimizing the model's performance. Finally, the paper provides several experimental results and analyses for the aspect-based sentiment analysis task in a few-shot setting. The constructed data and baseline model are available on AIHUB. (www.aihub.or.kr).
KMSS: Korean Media Script Dataset for Dialogue Summarization
Bong-Su Kim, Ji-Yoon Kim, Seung-ho Choi, Hyun-Kyu Jeon, Hye-Jin Jun, Hye-In Jung, Jung-Hoon Jang
http://doi.org/10.5626/JOK.2024.51.4.311
Dialogue summarization involves extracting or generating key contents from multi-turn documents consisting of utterances by multiple speakers. Dialogue summarization models are beneficial in analyzing content and service records for recommendations in conversation systems. However, there are no Korean dialogue summarization datasets necessary for model construction. This paper proposes a dataset for generative-based dialogue summarization. Source data were collected from the large-capacity contents of domestic broadcasters, and annotators manually labeled them. The dataset comprises approximately 100,000 entries across 6 categories, with summary sentences annotated as single sentences, three sentences, or two-and-a-half sentences. Additionally, this paper introduces a dialogue summary labeling guide to internalize and control data characteristics. It also presents a method for selecting a decoding model structure for model suitability verification. Through experiments, we highlight some characteristics of the constructed data and present benchmark performances for future research.
Interactive Visual Analytics System for Criminal Intelligence Analysts with Multiple Coordinated Views
Seokweon Jung, Donghwa Shin, Jinwook Bok, Seokhyeon Park, Hyeon Jeon, Jinwook Seo, Insoo Lee, Sooyoung Park
http://doi.org/10.5626/JOK.2023.50.1.47
Data that criminal intelligence analysts have to analyze have become much larger and more complex in recent decades. However, the environment and methods of investigation have not yet kept up with those changes. In this study, we examined current investigation practices in Korean Government Agency. We focused on the sensemaking process of investigation and tried to adopt visual analytics approaches for sensemaking into the investigation. We derived tasks and design requirements and designed a multi-view visual analytics system that could satisfy them. We validated our design with a high-fidelity prototype through a case study to show realistic use cases.
Design and Evaluation of Loss Functions based on Classification Models
Hyun-Kyu Jeon, Yun-Gyung Cheong
http://doi.org/10.5626/JOK.2021.48.10.1132
Paraphrase generation is a task in which the model generates an output sentence conveying the same meaning as the given input text but with a different representation. Recently, paraphrase generation has been widely used for solving the task of using artificial neural networks with supervised learning between the model’s prediction and labels. However, this method gives limited information because it only detects the representational difference. For that reason, we propose a method to extract semantic information with classification models and use them for the training loss function. Our evaluations showed that the proposed method outperformed baseline models.
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