@inproceedings{jang-etal-2021-kw,
title = "{KW}-{ATTN}: Knowledge Infused Attention for Accurate and Interpretable Text Classification",
author = "Jang, Hyeju and
Bang, Seojin and
Xiao, Wen and
Carenini, Giuseppe and
Ng, Raymond and
Lee, Young ji",
editor = "Agirre, Eneko and
Apidianaki, Marianna and
Vuli{\'c}, Ivan",
booktitle = "Proceedings of Deep Learning Inside Out (DeeLIO): The 2nd Workshop on Knowledge Extraction and Integration for Deep Learning Architectures",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.deelio-1.10",
doi = "10.18653/v1/2021.deelio-1.10",
pages = "96--107",
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.",
}
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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)
ACL