Restaurant: retaurant reviews from SemEval 2014 (task 4), SemEval 2015 (task 12) and SemEval 2016 (task 5)
Laptop: laptop reviews from SemEval 2014
Quick Start
Reproduce the results on Restaurant and Laptop dataset:
# train the model with 5 different seed numbers
python fast_run.py
Train the model on other ABSA dataset:
place data files in the directory ./data/[YOUR_DATASET_NAME].
set TASK_NAME in train.sh as [YOUR_DATASET_NAME].
train the model: sh train.sh
(** New feature **) Perform pure inference/direct transfer over test/unseen data using the trained ABSA model:
place data file in the directory ./data/[YOUR_EVAL_DATASET_NAME].
set TASK_NAME in work.sh as [YOUR_EVAL_DATASET_NAME]
set ABSA_HOME in work.sh as [HOME_DIRECTORY_OF_YOUR_ABSA_MODEL]
run: sh work.sh
Environment
OS: REHL Server 6.4 (Santiago)
GPU: NVIDIA GTX 1080 ti
CUDA: 10.0
cuDNN: v7.6.1
Citation
If the code is used in your research, please star our repo and cite our paper as follows:
@inproceedings{li-etal-2019-exploiting,
title = "Exploiting {BERT} for End-to-End Aspect-based Sentiment Analysis",
author = "Li, Xin and
Bing, Lidong and
Zhang, Wenxuan and
Lam, Wai",
booktitle = "Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)",
year = "2019",
url = "https://www.aclweb.org/anthology/D19-5505",
pages = "34--41"
}