Session: DAC-04-2: Data-Driven Design

Paper Number: 142891

142891 - Automated Sub-Feature Labeling Using Prompt-Based Pretrained Language Model 

Many studies have been utilizing online user-generated data
to draw product design implications via supervised and unsupervised
approaches. While the supervised learning methods
typically yield higher performance, they demand extensive
data labeling tasks, consuming significant time and effort. This
study proposes a framework that automatically labels online user
data to address this limitation. The proposed framework consists
of two pseudo-labeling mechanisms, keyword detection and
prompting Pretraied Language Model (PLM). The first stage defines
keywords for the target topic and then labels datasets by
checking if the data contains these keywords. The second stage
employs the PLM and labels datasets based on their context.
Specifically, Prompting PLM adds a task-specific template at the
end of the given text data (review) and predicts the masked token
(label). This PLM-based approach serves as promising labeling
candidates as they can make predictions without additional
training data from the target domain. The suggested method was
tested on a case study with real-world datasets. The study validates
the effectiveness of this novel framework by comparing
the pseudo-labeled results on smartphone sub-features to manual
ground-truths. The results demonstrate that the new framework
achieves F1 scores 28% and 14% higher than a baseline for screen and battery, respectively.

Presenting Author: Seyoung Park University of Illinois Urbana-Champaign

Presenting Author Biography: Seyoung Park received PhD in Industrial Engineering at the University of Illinois Urbana-Champaign. She is currently working as a postdoc at the University of Illinois.

Authors:

Seyoung Park University of Illinois Urbana-Champaign
Yilan Jiang University of Illinois Urbana-Champaign
Harrison Kim University Of Illinois