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Enhancing Passage Selection and Answer Generation in FiD Systems Using Relevance Gating

Seung-ho Choi, Shihyun Park, Minsang Kim, Chansol Park, Junho Wang, Ji-Yoon Kim, Bong-Su Kim

http://doi.org/10.5626/JOK.2025.52.5.385

In this paper, we proposed a novel approach to enhance the performance of the Fusion-in-Decoder (FiD) model in open-domain question answering systems. The FiD model operates by independently encoding multiple passages and then combining them during the decoding stage to generate answers. However, this method has the drawback of not filtering out passages containing unnecessary information, thereby placing an excessive burden on the decoder. To address this issue, we introduced a Relevance Gate inspired by the forget gate of Long Short-Term Memory (LSTM). This gate can evaluate the relevance of each passage in parallel, selectively transmitting information to the decoder, thereby significantly improving the accuracy and efficiency of answer generation. Additionally, we applied a new activation function suitable for open-domain question answering systems instead of the sigmoid function to ensure the model's stability.

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).


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