This repo contains the annotated data and code for our paper Aspect Sentiment Quad Prediction as Paraphrase Generation in EMNLP 2021.

• We aim to tackle the aspect sentiment quad prediction (ASQP) task: given a sentence, we predict all sentiment quads (aspect category, aspect term, opinion term, sentiment polarity)

• We release two new datasets, namely rest15 and rest16 under the data dir.
• Each data instance contains the original sentence, as well as a list of sentiment quads, separated by ####.
• The annotations are from the combination of the existing TASD data and ASTE data. We conduct further annotations to obtain the complete quad label for each sentence.
• You can also access the ABSA triplet data from the repo Generative-ABSA.

We highly recommend you to install the specified version of the following packages to avoid unnecessary troubles:

• transformers==4.0.0
• sentencepiece==0.1.91
• pytorch_lightning==0.8.1

• Set up the environment as described in the above section
• Download the pre-trained T5-base model (you can also use larger versions for better performance depending on the availability of the computation resource), put it under the folder T5-base.
  • You can also skip this step and the pre-trained model would be automatically downloaded to the cache in the next step
• Run command sh run.sh, which runs the ASQP task on the rest15 dataset.
• More details can be found in the paper and the help info in the main.py.

If the code is used in your research, please star our repo and cite our paper as follows:

@inproceedings{zhang-etal-2021-aspect-sentiment,
    title = "Aspect Sentiment Quad Prediction as Paraphrase Generation",
    author = "Zhang, Wenxuan  and
      Deng, Yang  and
      Li, Xin  and
      Yuan, Yifei  and
      Bing, Lidong  and
      Lam, Wai",
    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2021",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.emnlp-main.726",
    pages = "9209--9219",
}