An Empirical Study of Benchmarking Chinese Aspect Sentiment Quad
  Prediction
Junxian Zhou Haiqin Yang Ye Junpeng Yuxuan He Hao Mou
Aspect sentiment quad prediction (ASQP) is a critical subtask of aspect-level
sentiment analysis. Current ASQP datasets are characterized by their small size
and low quadruple density, which hinders technical development. To expand
capacity, we construct two large Chinese ASQP datasets crawled from multiple
online platforms. The datasets hold several significant characteristics: larger
size (each with 10,000+ samples) and rich aspect categories, more words per
sentence, and higher density than existing ASQP datasets. Moreover, we are the
first to evaluate the performance of Generative Pre-trained Transformer (GPT)
series models on ASQP and exhibit potential issues. The experiments with
state-of-the-art ASQP baselines underscore the need to explore additional
techniques to address ASQP, as well as the importance of further investigation
into methods to improve the performance of GPTs.