Augmentation of Chinese Character Representations with Compositional Graph Learning (Student Abstract)
DOI:
https://doi.org/10.1609/aaai.v36i11.21674Keywords:
Chinese Word Embedding, Natural Language Processing, Graph Representation Learning, InterpretabilityAbstract
Chinese characters have semantic-rich compositional information in radical form. While almost all previous research has applied CNNs to extract this compositional information, our work utilizes deep graph learning on a compact, graph-based representation of Chinese characters. This allows us to exploit temporal information within the strict stroke order used in writing characters. Our results show that our stroke-based model has potential for helping large-scale language models on some Chinese natural language understanding tasks. In particular, we demonstrate that our graph model produces more interpretable embeddings shown through word subtraction analogies and character embedding visualizations.Downloads
Published
2022-06-28
How to Cite
Wang, J., Fu, K., Chen, Z., & Lu, C.-T. (2022). Augmentation of Chinese Character Representations with Compositional Graph Learning (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 36(11), 13075-13076. https://doi.org/10.1609/aaai.v36i11.21674
Issue
Section
AAAI Student Abstract and Poster Program