Learning Span-Level Interactions for Aspect Sentiment Triplet Extraction

@inproceedings{Xu2021LearningSI,
  title={Learning Span-Level Interactions for Aspect Sentiment Triplet Extraction},
  author={Lu Xu and Lu Xu and Yew Ken Chia and Lidong Bing},
  booktitle={ACL/IJCNLP},
  year={2021}
}
Aspect Sentiment Triplet Extraction (ASTE) is the most recent subtask of ABSA which outputs triplets of an aspect target, its associated sentiment, and the corresponding opinion term. Recent models perform the triplet extraction in an end-to-end manner but heavily rely on the interactions between each target word and opinion word. Thereby, they cannot perform well on targets and opinions which contain multiple words. Our proposed span-level approach explicitly considers the interaction between… Expand
SentiPrompt: Sentiment Knowledge Enhanced Prompt-Tuning for Aspect-Based Sentiment Analysis
  • Chengxi Li, Feiyu Gao, +8 authors Zhi Yu
  • Computer Science
  • ArXiv
  • 2021
TLDR
SentiPrompt is proposed to use sentiment knowledge enhanced prompts to tune the language model in the unified framework and inject sentiment knowledge regarding aspects, opinions, and polarities into prompt and explicitly model term relations via constructing consistency and polarity judgment templates from the ground truth triplets. Expand

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