Solving Aspect Category Sentiment Analysis as a Text Generation Task
Jian Liu, Zhiyang Teng, Leyang Cui, Hanmeng Liu, Yue Zhang
Abstract
Aspect category sentiment analysis has attracted increasing research attention. The dominant methods make use of pre-trained language models by learning effective aspect category-specific representations, and adding specific output layers to its pre-trained representation. We consider a more direct way of making use of pre-trained language models, by casting the ACSA tasks into natural language generation tasks, using natural language sentences to represent the output. Our method allows more direct use of pre-trained knowledge in seq2seq language models by directly following the task setting during pre-training. Experiments on several benchmarks show that our method gives the best reported results, having large advantages in few-shot and zero-shot settings.
BibTeX
@inproceedings{liu-etal-2021-solving,
title = "Solving Aspect Category Sentiment Analysis as a Text Generation Task",
author = "Liu, Jian and
Teng, Zhiyang and
Cui, Leyang and
Liu, Hanmeng and
Zhang, Yue",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.361/",
doi = "10.18653/v1/2021.emnlp-main.361",
pages = "4406--4416"
}