SAECON

Sentiment Analysis Enhanced COmparative Network

Reference

The paper associated with this GitHub repo has been accepted by EMNLP'21 Findings. Please cite our paper via the following BibTex.

@inproceedings{saecon,
  author    = {Zeyu Li and
               Yilong Qin and
               Zihan Liu and
               Wei Wang},
  title     = {Powering Comparative Classification with Sentiment Analysis via Domain Adaptive Knowledge Transfer},
  booktitle = {Proceedings of the 2021 Conference on Empirical Methods in Natural
               Language Processing, 
               {EMNLP} 2021, 
               7-11 November 2021, 
               Online and in the Barceló Bávaro Convention Centre, Punta Cana, Dominican Republic},
  volume    = {{EMNLP} 2021},
  publisher = {Association for Computational Linguistics},
  year      = {2021},
}

Prerequisite

To run preprocess for SAECON, we need to get ready for a few things: datasets, Python packages, nltk corpora dependencies, pre-trained bert model, and glove pre-trained word vectors.

Data

Comparative Preference Classification (CPC)

The datasets are included in this repo. They are originally released by authors of Categorizing Comparative Sentences. Here's the GitHub. Many thanks to the authors!

Aspect-Based Sentiment Analysis (ABSA)

The ABSA datasets are included in this repo too. They are originally released in SemEval 2014 Task 4, 2015 Task 12, and 2016 Task5. You can find them here for 2014, 2015, and 2016. Many thanks to the task organizers and contributors!

We also borrowed code from this GitHub repo for processing SemEval datasets. Many thanks to the author Henry B. Moss!

Packages

We have a compiled command file to install all packages needed. It will install a few Python packages and nltk corpora.

bash get_ready.sh

Bert is also integrated in the package transformers. Our code will take care of downloading pretrained weights and tokenizers(many thanks to Hugging Face Team who made this possible!).

There's a special requirement for PyTorch-geometric which help GCN implementation. On our machine, we have PyTorch version 1.8.1+cu102 and CUDA version 10.2. Therefore, we replace ${TORCH}+${CUDA} with 1.8.1+cu102.

pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-${TORCH}+${CUDA}.html
pip install torch-sparse -f https://pytorch-geometric.com/whl/torch-${TORCH}+${CUDA}.html
pip install torch-geometric

GloVe

Please download pretrained GloVe word embeddings from here. The dimension is open to selection. However, GloVe embedding format doesn't inherently work well with KeyedVectors class in gensim. So please first convert GloVe embeddings to word2vec format following this link. Transformed GloVe embedding dumps should be placed in ./data/glove/glove.6B.[dim]d.word2vec_format.txt. The [dim] should be replaced by the dimension of selection.

Cong! You are all set.

Preprocess

We first convert all raw data files into something that fit our implementation. Run

python src/preprocess.py [--options]

Use -h to see all options. For the first time, run this:

python src/preprocess.py \
    --gpu_id 1 \
    --process_cpc_instances \
    --process_absa_instances \
    --generate_bert_emb \
    --generate_glove_emb \
    --generate_dep_graph \
    --generate_aspect_dist

Output files are saved in ./data/.

Training

To run the training pipelines, use

python src/train.py [--options]

Use -h to see all options. Example usage:

python src/train.py \
    --task train \
    --gpu_id 0 \
    --use_lr_scheduler \
    --input_emb fix \
    --emb_dim 768 \
    --feature_dim 120 \
    --lr 0.0005 \
    --absa_lr 0.002 \
    --reg_weight 0.00001 \
    --dropout 0.1 \
    --num_ep 50 \
    --batch_size 16 \
    --batch_ratio 1:1 \
    --eval_per_ep 1 \
    --eval_after_epnum 1 \
    --sgcn_dims 256 \
    --sgcn_gating \
    --sgcn_directed \
    --log_batch_num 1 \
    --absa_log_batch_num 1 

Testing