Papers by Yi-Hsin Chen

1 papers
ConTextING: Granting Document-Wise Contextual Embeddings to Graph Neural Networks for Inductive Text Classification (2022.coling-1)

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Challenge: Graph neural networks (GNNs) are used to learn document representation from graph structures.
Approach: They propose a unified model with a joint training mechanism to learn from document embeddings and contextual word interactions simultaneously.
Outcome: The proposed model outperforms pure inductive GNNs and BERT-style models . the proposed model also has a joint training mechanism to learn from document embeddings and contextual word interactions simultaneously.

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