This repo contains the data and code for our paper Towards Generative Aspect-Based Sentiment Analysis in ACL 2021.
Pls note that some packages (such as transformers) are under highly active development, so we highly recommend you to install the specified version of the following packages:
T5-base
.
sh run.sh
, which runs the UABSA
task on the laptop14
dataset.We conduct experiments on four ABSA tasks with four datasets in the paper, you can change the parameters in run.sh
to try them:
python main.py --task $task \
--dataset $dataset \
--model_name_or_path t5-base \
--paradigm $paradigm \
--n_gpu 0 \
--do_train \
--do_direct_eval \
--train_batch_size 16 \
--gradient_accumulation_steps 2 \
--eval_batch_size 16 \
--learning_rate 3e-4 \
--num_train_epochs 20
$task
refers to one of the ABSA task in [aope
, uabsa
, aste
, tasd
] $dataset
refers to one of the four datasets in [laptop14
, rest14
, rest15
, rest6
]$paradigm
refers to one of the two paradigms proposed in the model. 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-towards,
title = "Towards Generative Aspect-Based Sentiment Analysis",
author = "Zhang, Wenxuan and
Li, Xin and
Deng, Yang and
Bing, Lidong and
Lam, Wai",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
year = "2021",
url = "https://aclanthology.org/2021.acl-short.64",
pages = "504--510",
}