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.
- 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
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.pyWe 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.pySome of the RGCN functionalities are adapted from https://github.com/JinheonBaek/RGCN
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.