Name
Abstract | ||
|---|---|---|
Existing studies typically handle aspect-based sentiment analysis by stacking multiple neural modules, which inevitably result in severe error propagation. Instead, we propose a novel end-to-end framework, MRCOOL: MRC-PrOmpt mOdeL framework, where numerous sentiment aspects are elicited by a machine reading comprehension (MRC) model and their corresponding sentiment polarities are classified in a prompt learning way. Experiments show that our end-to-end framework consistently yields promising results on widely-used benchmark datasets which significantly outperform existing state-of-the-art models or achieve comparable performance. |
Year | Venue | DocType |
|---|---|---|
2022 | International Conference on Computational Linguistics | Conference |
Volume | Citations | PageRank |
Proceedings of the 29th International Conference on Computational Linguistics | 0 | 0.34 |
References | Authors | |
0 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yifei Yang | 1 | 0 | 0.34 |
| Hai Zhao | 2 | 960 | 113.64 |