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Are you interested in ABSA? Welcome to view our curated ABSA-Reading-List .

Are you troubled by no or few labeled data for ABSA? Feel free to check out our latest work, FS-ABSA , accepted by SIGIR 2023.

Overview

In this work, we present UnifiedABSA, a general-purpose multi-task ABSA framework based on multi-task instruction-tuning. Specifically,

  • we formulate all ABSA tasks as a conditional generation problem.
  • we design unified sentiment instructions (USI) for each task to help the model distinguish between various ABSA tasks.

Citation

If you find this work helpful, please cite our paper as follows:

@article{wang2022unifiedabsa,
  author       = {Zengzhi Wang and
                  Rui Xia and
                  Jianfei Yu},
  title        = {UnifiedABSA: {A} Unified {ABSA} Framework Based on Multi-task Instruction
                  Tuning},
  journal      = {CoRR},
  volume       = {abs/2211.10986},
  year         = {2022},
  url          = {https://doi.org/10.48550/arXiv.2211.10986},
  doi          = {10.48550/arXiv.2211.10986},
  eprinttype    = {arXiv},
  eprint       = {2211.10986},
}

Any Questions?

If you have any questions related to this work, you can open an issue with details or feel free to email Zengzhi(zzwang@njust.edu.cn).

Acknowledgements

Our code is based on ABSA-QUAD. Thanks for their work.

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