Towards Generative Aspect-Based Sentiment Analysis
Aspect-based sentiment analysis (ABSA) has received increasing attention recently. Most existing work tackles ABSA in a discriminative manner, designing various task-specific classification networks for the prediction. Despite their effectiveness, these methods ignore the rich label semantics in ABSA problems and require extensive task-specific designs. In this paper, we propose to tackle various ABSA tasks in a unified generative framework. Two types of paradigms, namely annotation-style and extraction-style modeling, are designed to enable the training process by formulating each ABSA task as a text generation problem. We conduct experiments on four ABSA tasks across multiple benchmark datasets where our proposed generative approach achieves new state-of-the-art results in almost all cases. This also validates the strong generality of the proposed framework which can be easily adapted to arbitrary ABSA task without additional taskspecific model design.
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Datasets
Results from the Paper
| Task | Dataset | Model | Metric Name | Metric Value | Global Rank | Benchmark |
|---|---|---|---|---|---|---|
| Aspect-Based Sentiment Analysis (ABSA) | ASQP | GAS | F1 (R15) | 45.98 | # 7 | |
| F1 (R16) | 56.03 | # 7 | ||||
| Aspect-Based Sentiment Analysis (ABSA) | ASTE | GAS | F1 (L14) | 58.19 | # 9 | |
| F1(R14) | 70.52 | # 9 | ||||
| F1 (R15) | 60.23 | # 9 | ||||
| F1 (R16) | 69.05 | # 9 | ||||
| Aspect Sentiment Triplet Extraction | ASTE-Data-V2 | GAS | F1 | 72.16 | # 4 | |
| Aspect-Based Sentiment Analysis (ABSA) | TASD | GAS | F1 (R15) | 60.63 | # 6 | |
| F1 (R16) | 68.31 | # 6 |
