Open AccessPrompt-Based Label-Aware Framework for Few-Shot Multi-Label Text Classification
Author(s) -
Thanakorn Thaminkaew,
Piyawat Lertvittayakumjorn,
Peerapon Vateekul
Publication year - 2024
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2024.3367994
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
Prompt-based learning has demonstrated remarkable success in few-shot text classification, outperforming the traditional fine-tuning approach. This method transforms a text input into a masked language modeling prompt using a template, queries a fine-tuned language model to fill in the mask, and then uses a verbalizer to map the model’s output to a predicted class. Previous prompt-based text classification approaches were primarily designed for multi-class classification, taking advantage of the fact that the classes are mutually exclusive and one example belongs to only one class. However, these assumptions do not hold in the context of multi-label text classification, where labels often exhibit correlations with each other. Therefore, we propose a Prompt-based Label-Aware framework for Multi-Label text classification (PLAML) that addresses the challenges. Specifically, PLAML enhances prompt-based learning with three proposed techniques to improve the overall performance for multi-label classification. The techniques include ( i ) a token weighting algorithm that considers the correlations between labels, ( ii ) a template for augmenting training samples, making the training process label-aware, and ( iii ) a dynamic threshold mechanism, refining the prediction condition of each label. Extensive experiments on few-shot text classification across multiple datasets with various languages show that our PLAML outperforms other baseline methods. We also analyzed the effect of each proposed technique to better understand how it is suitable for the multi-label setting.