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    <abstract>Graph neural networks (GNNs) have been recently applied in natural language processing. Various GNN research studies are proposed to learn node interactions within the local graph of each document that contains words, sentences, or topics for inductive text classification. However, most inductive GNNs that are built on a word graph generally take global word embeddings as node features, without referring to document-wise contextual information. Consequently, we find that BERT models can perform better than inductive GNNs. An intuitive follow-up approach is used to enrich GNNs with contextual embeddings from BERT, yet there is a lack of related research. In this work, we propose a simple yet effective unified model, coined ConTextING, with a joint training mechanism to learn from both document embeddings and contextual word interactions simultaneously. Our experiments show that ConTextING outperforms pure inductive GNNs and BERT-style models. The analyses also highlight the benefits of the sub-word graph and joint training with separated classifiers.</abstract>
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%0 Conference Proceedings
%T ConTextING: Granting Document-Wise Contextual Embeddings to Graph Neural Networks for Inductive Text Classification
%A Huang, Yen-Hao
%A Chen, Yi-Hsin
%A Chen, Yi-Shin
%S Proceedings of the 29th International Conference on Computational Linguistics
%D 2022
%8 October
%I International Committee on Computational Linguistics
%C Gyeongju, Republic of Korea
%F huang-etal-2022-contexting
%X Graph neural networks (GNNs) have been recently applied in natural language processing. Various GNN research studies are proposed to learn node interactions within the local graph of each document that contains words, sentences, or topics for inductive text classification. However, most inductive GNNs that are built on a word graph generally take global word embeddings as node features, without referring to document-wise contextual information. Consequently, we find that BERT models can perform better than inductive GNNs. An intuitive follow-up approach is used to enrich GNNs with contextual embeddings from BERT, yet there is a lack of related research. In this work, we propose a simple yet effective unified model, coined ConTextING, with a joint training mechanism to learn from both document embeddings and contextual word interactions simultaneously. Our experiments show that ConTextING outperforms pure inductive GNNs and BERT-style models. The analyses also highlight the benefits of the sub-word graph and joint training with separated classifiers.
%U https://aclanthology.org/2022.coling-1.100
%P 1163-1168

Markdown (Informal)

[ConTextING: Granting Document-Wise Contextual Embeddings to Graph Neural Networks for Inductive Text Classification](https://aclanthology.org/2022.coling-1.100) (Huang et al., COLING 2022)

• ConTextING: Granting Document-Wise Contextual Embeddings to Graph Neural Networks for Inductive Text Classification (Huang et al., COLING 2022)

ACL

• Yen-Hao Huang, Yi-Hsin Chen, and Yi-Shin Chen. 2022. ConTextING: Granting Document-Wise Contextual Embeddings to Graph Neural Networks for Inductive Text Classification. In Proceedings of the 29th International Conference on Computational Linguistics, pages 1163–1168, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.