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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).
Graph Neural Networks with Prototype Nodes for Few-shot Image Classification
http://doi.org/10.5626/JOK.2023.50.2.127
The remarkable performance of deep learning models is based on a large amount of training data. However, there are a number of domains where it is difficult to obtain such a large amount of data, and in these domains a large amount of resources must be invested for data collection and refining. To overcome these limitations, research on few-shot learning, which enables learning with only a small number of data, is being actively conducted. In particular, among meta learning methodologies, metric-based learning which utilizes similarity between data has the advantage that it does not require fine-tuning of the model for a new task, and recent studies using graph neural networks have shown good results. A few-shot classification model based on a graph neural network can explicitly process data characteristics and the relationship between data by constructing a task graph using data of a given support set and query set as nodes. The EGNN(Edge-labeling Graph Neural Net) model expresses the similarity between data in the form of edge labels and models the intra-class and inter-class similarity more clearly. In this paper, we propose a method of applying a prototype node representing each class to few-shot task graph to model the similarity between data and class-data at the same time. The proposed model provides a generalized prototype node that is created based on task data and class configuration, and it can perform two different few-shot image classification predictions based on the prototype-query edge label or the Euclidean distance between prototype-query nodes. Comparing the 5-way 5-shot classification performance on the mini-ImageNet dataset with the EGNN model and other meta-learning-based few-shot classification models, the proposed model showed significant performance improvement.
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