RACL

Code and datasets of our paper "Relation-Aware Collaborative Learning for Unified Aspect-Based Sentiment Analysis" accepted by ACL 2020.

1. Requirements

To reproduce the reported results accurately, please install the specific version of each package.

• python 3.6.10
• tensorflow-gpu 1.5.0
• numpy 1.16.4
• scikit-learn 0.22.2

2. Usage

We incorporate the training and evaluation of RACL in the train_racl.py. Just run it as below.

CUDA_VISIBLE_DEVICES=0 python train_racl.py --task res14 --load 0

We use the datasets pre-processed by IMN. The results of 5 runs should be like this.

Res14	AE-F1	OE-F1	SC-F1	ABSA-F1
round1	85.38	85.31	74.72	70.80
round2	85.41	85.03	74.58	70.72
round3	85.20	85.55	74.26	70.43
round4	85.20	85.29	74.19	70.43
round5	85.68	85.42	74.56	70.96
AVG	85.37	85.32	74.46	70.67

3. Checkpoints

If you have problems in training RACL, you can also use the released pre-trained weights on the following links.

• Google Drive : Click here.
• Baidu Cloud : Click here. (password:78qy)

Unzip the pre-trained weights in the checkpoint folder, and execute the command as below.

CUDA_VISIBLE_DEVICES=0 python train_racl.py --task res14 --load 1

The results after loading weights should be like this.

Dataset	AE-F1	OE-F1	SC-F1	ABSA-F1
Res14	85.33	86.09	76.31	71.61
Lap14	82.81	77.91	73.04	62.59
Res15	71.76	76.74	67.74	61.20

4. Embeddings

We have generated the word-idx mapping file and the global-purpose & domain-specific embeddings in the data folder. If you want to generate them from scratch, follow the steps below.

• Download glove.840B.300d.txt, rename it to glove_emb.txt, and put it in the folder like ./data/res14.
• Download domain-specific vectors proposed by DE-CNN, rename it to domain_emb.txt, and put it in the folder like ./data/res14. Notice that restaurant_emb.vec is for datasets Res14 and Res15, and laptop_emb.vec is for Lap14.
• Specify the task (i.e., Res14) in embedding.py, then run it.
• Word embeddings will be generated in the corresponding folder, e.g., ./data/res14/glove_embedding.npy.

5. Data Details

A separate set consists of four files:

• sentence.txt contains the tokenized review sentences.
• target.txt contains the aspect term tag sequences. 0=O, 1=B, 2=I.
• opnion.txt contains the opinion term tag sequences. 0=O, 1=B, 2=I.
• target_polarity.txt contains the sentiment tag sequences. 0=background, 1=positive, 2=negative, 3=neutral, 4=conflict.

6. Need Better Results?

If you still need a better performance of RACL, you can increase the $hop_num$ argument in train_racl.py. Stacking layers to 5~6 can introduce 1~2% absolute improvements on ABSA-F1.

7. RACL-BERT (2020.09.16 update)

We have updated the files for RACL-BERT. To run it, follow the steps below.

• Upgrade the version of tensorflow-gpu to 1.12.0. (The lower version could deteriorate the performance.)

• Download the checkpoint of BERT-Large, then unzip it in the folder ./bert-large/ (e.g., ./bert-large/bert_config.json).

• Execute the command as below.

   CUDA_VISIBLE_DEVICES=0 python train_racl_bert.py --task res14 --load 0
  

The results of RACL-BERT should be like this.

Dataset	AE-F1	OE-F1	SC-F1	ABSA-F1
Res14	87.55	86.21	81.41	76.25
Lap14	82.24	79.19	75.05	65.67
Res15	74.20	74.58	75.65	66.07

8. Citation

If you find our code and datasets useful, please cite our paper.

@inproceedings{chen2020racl,
  author    = {Zhuang Chen and Tieyun Qian},
  title     = {Relation-Aware Collaborative Learning for Unified Aspect-Based Sentiment Analysis},
  booktitle = {ACL},
  pages     = {3685-3694},
  year      = {2020},
  url       = {https://www.aclweb.org/anthology/2020.acl-main.340}
}

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