@inproceedings{huang-etal-2021-reasoning,
title = "Reasoning over Entity-Action-Location Graph for Procedural Text Understanding",
author = "Huang, Hao and
Geng, Xiubo and
Pei, Jian and
Long, Guodong and
Jiang, Daxin",
editor = "Zong, Chengqing and
Xia, Fei and
Li, Wenjie and
Navigli, Roberto",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-long.396",
doi = "10.18653/v1/2021.acl-long.396",
pages = "5100--5109",
abstract = "Procedural text understanding aims at tracking the states (e.g., create, move, destroy) and locations of the entities mentioned in a given paragraph. To effectively track the states and locations, it is essential to capture the rich semantic relations between entities, actions, and locations in the paragraph. Although recent works have achieved substantial progress, most of them focus on leveraging the inherent constraints or incorporating external knowledge for state prediction. The rich semantic relations in the given paragraph are largely overlooked. In this paper, we propose a novel approach (REAL) to procedural text understanding, where we build a general framework to systematically model the entity-entity, entity-action, and entity-location relations using a graph neural network. We further develop algorithms for graph construction, representation learning, and state and location tracking. We evaluate the proposed approach on two benchmark datasets, ProPara, and Recipes. The experimental results show that our method outperforms strong baselines by a large margin, i.e., 5.0{\%} on ProPara and 3.2{\%} on Recipes, illustrating the utility of semantic relations and the effectiveness of the graph-based reasoning model.",
}
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<abstract>Procedural text understanding aims at tracking the states (e.g., create, move, destroy) and locations of the entities mentioned in a given paragraph. To effectively track the states and locations, it is essential to capture the rich semantic relations between entities, actions, and locations in the paragraph. Although recent works have achieved substantial progress, most of them focus on leveraging the inherent constraints or incorporating external knowledge for state prediction. The rich semantic relations in the given paragraph are largely overlooked. In this paper, we propose a novel approach (REAL) to procedural text understanding, where we build a general framework to systematically model the entity-entity, entity-action, and entity-location relations using a graph neural network. We further develop algorithms for graph construction, representation learning, and state and location tracking. We evaluate the proposed approach on two benchmark datasets, ProPara, and Recipes. The experimental results show that our method outperforms strong baselines by a large margin, i.e., 5.0% on ProPara and 3.2% on Recipes, illustrating the utility of semantic relations and the effectiveness of the graph-based reasoning model.</abstract>
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%0 Conference Proceedings
%T Reasoning over Entity-Action-Location Graph for Procedural Text Understanding
%A Huang, Hao
%A Geng, Xiubo
%A Pei, Jian
%A Long, Guodong
%A Jiang, Daxin
%Y Zong, Chengqing
%Y Xia, Fei
%Y Li, Wenjie
%Y Navigli, Roberto
%S Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F huang-etal-2021-reasoning
%X Procedural text understanding aims at tracking the states (e.g., create, move, destroy) and locations of the entities mentioned in a given paragraph. To effectively track the states and locations, it is essential to capture the rich semantic relations between entities, actions, and locations in the paragraph. Although recent works have achieved substantial progress, most of them focus on leveraging the inherent constraints or incorporating external knowledge for state prediction. The rich semantic relations in the given paragraph are largely overlooked. In this paper, we propose a novel approach (REAL) to procedural text understanding, where we build a general framework to systematically model the entity-entity, entity-action, and entity-location relations using a graph neural network. We further develop algorithms for graph construction, representation learning, and state and location tracking. We evaluate the proposed approach on two benchmark datasets, ProPara, and Recipes. The experimental results show that our method outperforms strong baselines by a large margin, i.e., 5.0% on ProPara and 3.2% on Recipes, illustrating the utility of semantic relations and the effectiveness of the graph-based reasoning model.
%R 10.18653/v1/2021.acl-long.396
%U https://aclanthology.org/2021.acl-long.396
%U https://doi.org/10.18653/v1/2021.acl-long.396
%P 5100-5109
Markdown (Informal)
[Reasoning over Entity-Action-Location Graph for Procedural Text Understanding](https://aclanthology.org/2021.acl-long.396) (Huang et al., ACL-IJCNLP 2021)
ACL
- Hao Huang, Xiubo Geng, Jian Pei, Guodong Long, and Daxin Jiang. 2021. Reasoning over Entity-Action-Location Graph for Procedural Text Understanding. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 5100–5109, Online. Association for Computational Linguistics.