Towards Generative Aspect-Based Sentiment Analysis

ACL 2021  ·  Wenxuan Zhang, Xin Li, Yang Deng, Lidong Bing, Wai Lam ·

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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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


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