Prior-Bert and Multi-Task Learning for Target-Aspect-Sentiment Joint Detection

Cai Ke; Qingyu Xiong; Chao Wu; Zikai Liao; Hualing Yi

ICASSP 2022accepted0 citations

Prior-Bert and Multi-Task Learning for Target-Aspect-Sentiment Joint Detection

Cai Ke, Qingyu Xiong, Chao Wu, Zikai Liao, Hualing Yi

Abstract

Aspect-Based Sentiment Analysis (ABSA) is a fine-grained sentiment analysis task and has become a significant task with real-world scenario value. The challenge of this task is how to generate an effective text representation and construct an end-to-end model that can simultaneously detect (target, aspect, sentiment) triples from a sentence. Besides, the existing models do not take the heavily unbalanced distribution of labels into account and also do not give enough consideration to long-distance dependence of targets and aspect-sentiment pairs. To overcome these challenges, we propose a novel end-to-end model named Prior-BERT and Multi-Task Learning (PBERT-MTL), which can detect all triples more efficiently. We evaluate our model on SemEval-2015 and SemEval-2016 datasets. Extensive results show the validity of our work in this paper. In addition, our model also achieves higher performance on a series of subtasks of target-aspect-sentiment detection. Code is available at https://github.com/CQUPT-CaiKe/PBERT-MTL.

BibTeX

@inproceedings{icassp2022_priorbertandmult,
  title = {Prior-Bert and Multi-Task Learning for Target-Aspect-Sentiment Joint Detection},
  author = {Cai Ke and Qingyu Xiong and Chao Wu and Zikai Liao and Hualing Yi},
  booktitle = {ICASSP 2022},
  year = {2022}
}