The robustness of a model for real-world deployment is decided by how well it performs on unseen data and distinguishes between in-domain and out-of-domain samples. Visual document classifiers have shown impressive performance on in-distribution test sets. However, they tend to have a hard time correctly classifying and differentiating out-of-distribution examples. Image-based classifiers lack the text component, whereas multi-modality transformer-based models face the token serialization problem in visual documents due to their diverse layouts. They also require a lot of computing power during inference, making them impractical for many real-world applications. In this work, we propose a graph-based document classification model that addresses both of these challenges. Our approach generates a document graph based on its layout, and then trains a graph neural network to learn node and graph embeddings. Through experiments, we show that our model, even with fewer parameters, outperforms state-of-the-art models on out-of-distribution data while retaining comparable performance on the in-distribution test set.
@article{mohbat2023GVdoc,
title={GVdoc -- Graph-based Visual DOcument Classification},
author={Fnu Mohbat, Mohammed J. Zaki, Catherine Finegan-Dollak, Ashish Verma},
booktitle = {Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (ACL)},
year = {2023}
}