Low-Resource Clickbait Spoiling for Indonesian via Question Answering
Ni Putu Intan Maharani Ayu Purwarianti Alham Fikri Aji
Clickbait spoiling aims to generate a short text to satisfy the curiosity
induced by a clickbait post. As it is a newly introduced task, the dataset is
only available in English so far. Our contributions include the construction of
manually labeled clickbait spoiling corpus in Indonesian and an evaluation on
using cross-lingual zero-shot question answering-based models to tackle
clikcbait spoiling for low-resource language like Indonesian. We utilize
selection of multilingual language models. The experimental results suggest
that XLM-RoBERTa (large) model outperforms other models for phrase and passage
spoilers, meanwhile, mDeBERTa (base) model outperforms other models for
multipart spoilers.