CHEER: Centrality-aware high-order event reasoning network for document-level event causality identification

Document-level Event Causality Identification (DECI) aims to recognize causal relations between events within a document. Recent studies focus on building a document-level graph for cross-sentence reasoning, but ignore important causal structures — there are one or two “central” events that prevail...

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Main Authors: CHEN, Meiqi, CAO, Yixin, ZHANG, Yan, LIU, Zhiwei
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Language:English
Published: Institutional Knowledge at Singapore Management University 2023
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Online Access:https://ink.library.smu.edu.sg/sis_research/8287
https://ink.library.smu.edu.sg/context/sis_research/article/9290/viewcontent/2023.acl_long.604__1_.pdf
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spelling sg-smu-ink.sis_research-92902023-11-10T08:26:28Z CHEER: Centrality-aware high-order event reasoning network for document-level event causality identification CHEN, Meiqi CAO, Yixin ZHANG, Yan LIU, Zhiwei Document-level Event Causality Identification (DECI) aims to recognize causal relations between events within a document. Recent studies focus on building a document-level graph for cross-sentence reasoning, but ignore important causal structures — there are one or two “central” events that prevail throughout the document, with most other events serving as either their cause or consequence. In this paper, we manually annotate central events for a systematical investigation and propose a novel DECI model, CHEER, which performs high-order reasoning while considering event centrality. First, we summarize a general GNN-based DECI model and provide a unified view for better understanding. Second, we design an Event Interaction Graph (EIG) involving the interactions among events (e.g., coreference) and event pairs, e.g., causal transitivity, cause(A, B) AND cause(B, C) → cause(A, C). Finally, we incorporate event centrality information into the EIG reasoning network via well-designed features and multi-task learning. We have conducted extensive experiments on two benchmark datasets. The results present great improvements (5.9% F1 gains on average) and demonstrate the effectiveness of each main component. 2023-07-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/8287 info:doi/10.18653/v1/2023.acl-long.604 https://ink.library.smu.edu.sg/context/sis_research/article/9290/viewcontent/2023.acl_long.604__1_.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University benchmark datasets; causal relations; coreference; high-order; higher-order; identification modeling; interaction graphs; level graphs; multitask learning; ordering events Computer Sciences OS and Networks
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic benchmark datasets; causal relations; coreference; high-order; higher-order; identification modeling; interaction graphs; level graphs; multitask learning; ordering events
Computer Sciences
OS and Networks
spellingShingle benchmark datasets; causal relations; coreference; high-order; higher-order; identification modeling; interaction graphs; level graphs; multitask learning; ordering events
Computer Sciences
OS and Networks
CHEN, Meiqi
CAO, Yixin
ZHANG, Yan
LIU, Zhiwei
CHEER: Centrality-aware high-order event reasoning network for document-level event causality identification
description Document-level Event Causality Identification (DECI) aims to recognize causal relations between events within a document. Recent studies focus on building a document-level graph for cross-sentence reasoning, but ignore important causal structures — there are one or two “central” events that prevail throughout the document, with most other events serving as either their cause or consequence. In this paper, we manually annotate central events for a systematical investigation and propose a novel DECI model, CHEER, which performs high-order reasoning while considering event centrality. First, we summarize a general GNN-based DECI model and provide a unified view for better understanding. Second, we design an Event Interaction Graph (EIG) involving the interactions among events (e.g., coreference) and event pairs, e.g., causal transitivity, cause(A, B) AND cause(B, C) → cause(A, C). Finally, we incorporate event centrality information into the EIG reasoning network via well-designed features and multi-task learning. We have conducted extensive experiments on two benchmark datasets. The results present great improvements (5.9% F1 gains on average) and demonstrate the effectiveness of each main component.
format text
author CHEN, Meiqi
CAO, Yixin
ZHANG, Yan
LIU, Zhiwei
author_facet CHEN, Meiqi
CAO, Yixin
ZHANG, Yan
LIU, Zhiwei
author_sort CHEN, Meiqi
title CHEER: Centrality-aware high-order event reasoning network for document-level event causality identification
title_short CHEER: Centrality-aware high-order event reasoning network for document-level event causality identification
title_full CHEER: Centrality-aware high-order event reasoning network for document-level event causality identification
title_fullStr CHEER: Centrality-aware high-order event reasoning network for document-level event causality identification
title_full_unstemmed CHEER: Centrality-aware high-order event reasoning network for document-level event causality identification
title_sort cheer: centrality-aware high-order event reasoning network for document-level event causality identification
publisher Institutional Knowledge at Singapore Management University
publishDate 2023
url https://ink.library.smu.edu.sg/sis_research/8287
https://ink.library.smu.edu.sg/context/sis_research/article/9290/viewcontent/2023.acl_long.604__1_.pdf
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