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    <abstract>Text classification has wide-ranging applications in various domains. While neural network approaches have drastically advanced performance in text classification, they tend to be powered by a large amount of training data, and interpretability is often an issue. As a step towards better accuracy and interpretability especially on small data, in this paper we present a new knowledge-infused attention mechanism, called KW-ATTN (KnoWledge-infused ATTentioN) to incorporate high-level concepts from external knowledge bases into Neural Network models. We show that KW-ATTN outperforms baseline models using only words as well as other approaches using concepts by classification accuracy, which indicates that high-level concepts help model prediction. Furthermore, crowdsourced human evaluation suggests that additional concept information helps interpretability of the model.</abstract>
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%0 Conference Proceedings
%T KW-ATTN: Knowledge Infused Attention for Accurate and Interpretable Text Classification
%A Jang, Hyeju
%A Bang, Seojin
%A Xiao, Wen
%A Carenini, Giuseppe
%A Ng, Raymond
%A Lee, Young ji
%Y Agirre, Eneko
%Y Apidianaki, Marianna
%Y Vulić, Ivan
%S Proceedings of Deep Learning Inside Out (DeeLIO): The 2nd Workshop on Knowledge Extraction and Integration for Deep Learning Architectures
%D 2021
%8 June
%I Association for Computational Linguistics
%C Online
%F jang-etal-2021-kw
%X Text classification has wide-ranging applications in various domains. While neural network approaches have drastically advanced performance in text classification, they tend to be powered by a large amount of training data, and interpretability is often an issue. As a step towards better accuracy and interpretability especially on small data, in this paper we present a new knowledge-infused attention mechanism, called KW-ATTN (KnoWledge-infused ATTentioN) to incorporate high-level concepts from external knowledge bases into Neural Network models. We show that KW-ATTN outperforms baseline models using only words as well as other approaches using concepts by classification accuracy, which indicates that high-level concepts help model prediction. Furthermore, crowdsourced human evaluation suggests that additional concept information helps interpretability of the model.
%R 10.18653/v1/2021.deelio-1.10
%U https://aclanthology.org/2021.deelio-1.10
%U https://doi.org/10.18653/v1/2021.deelio-1.10
%P 96-107

Markdown (Informal)

[KW-ATTN: Knowledge Infused Attention for Accurate and Interpretable Text Classification](https://aclanthology.org/2021.deelio-1.10) (Jang et al., DeeLIO 2021)

• KW-ATTN: Knowledge Infused Attention for Accurate and Interpretable Text Classification (Jang et al., DeeLIO 2021)

ACL

• Hyeju Jang, Seojin Bang, Wen Xiao, Giuseppe Carenini, Raymond Ng, and Young ji Lee. 2021. KW-ATTN: Knowledge Infused Attention for Accurate and Interpretable Text Classification. In Proceedings of Deep Learning Inside Out (DeeLIO): The 2nd Workshop on Knowledge Extraction and Integration for Deep Learning Architectures, pages 96–107, Online. Association for Computational Linguistics.