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KinGDOM: Knowledge-Guided DOMain adaptation for sentiment analysis (ACL 2020)

KinGDOM takes a novel perspective on the task of domain adaptation in sentiment analysis by exploring the role of external commonsense knowledge. It utilizes the ConceptNet knowledge graph to enrich the semantics of a document by providing both domain-specific and domain-general background concepts. These concepts are learned by training a graph convolutional autoencoder that leverages inter-domain concepts in a domain-invariant manner. Conditioning a popular domain-adversarial baseline method with these learned concepts helps improve its performance over state-of-the-art approaches, demonstrating the efficacy of the proposed framework.

Alt text

Requirements

  • scipy==1.3.1
  • gensim==3.8.1
  • torch==1.6.0
  • numpy==1.18.2
  • scikit_learn==0.22.2.post1
  • torch_geometric==1.6.3

Execution

Download ConceptNet filtered for English language from here and keep in this root directory.

Preprocess, train and extract graph features:

python preprocess_graph.py
python train_and_extract_graph_features.py

We provide pretrained graph features in the graph_features directory. Note that, executing the above commands will overwrite the provided feature files.

Train the main domain adaptation model:

python train.py

Some of the RGCN functionalities are adapted from https://github.com/JinheonBaek/RGCN

Citation

Please cite the following paper if you find this code useful in your work.

KinGDOM: Knowledge-Guided DOMain adaptation for sentiment analysis. D. Ghosal, D. Hazarika, N. Majumder, A. Roy, S. Poria, R. Mihalcea. ACL 2020.