@inproceedings{wang-etal-2026-semeval,
title = "{S}em{E}val-2026 Task 12: Knowledge Graph with hyperbolic embedding in Abductive Event Reasoning",
author = "Wang, Mingkai and
Ojha, Varun and
Liang, Huizhi",
editor = "Kochmar, Ekaterina and
Ghosh, Debanjan and
North, Kai and
Komachi, Mamoru",
booktitle = "Proceedings of the 20th {I}nternational {W}orkshop on {S}emantic {E}valuation (2026)",
month = jul,
year = "2026",
address = "San Diego, California, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.semeval-1.114/",
pages = "821--825",
ISBN = "979-8-89176-414-9",
abstract = "This task introduces Abductive Event Reasoning (AER), a novel shared task, to investigate the ability of Large Language Models(LLMs) to reason about the causality of real-world events. More specifically, a data set consisting of different topics and choices is introduced, and we need to enable the model to select the best options for the given event. Three methods are separately introduced to explore thequestion, including the traditional natural language processing(NLP) method (DeBERTa), theenhanced knowledge graph(KG), and the KG embedded in hyperbolic space."
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<abstract>This task introduces Abductive Event Reasoning (AER), a novel shared task, to investigate the ability of Large Language Models(LLMs) to reason about the causality of real-world events. More specifically, a data set consisting of different topics and choices is introduced, and we need to enable the model to select the best options for the given event. Three methods are separately introduced to explore thequestion, including the traditional natural language processing(NLP) method (DeBERTa), theenhanced knowledge graph(KG), and the KG embedded in hyperbolic space.</abstract>
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%0 Conference Proceedings
%T SemEval-2026 Task 12: Knowledge Graph with hyperbolic embedding in Abductive Event Reasoning
%A Wang, Mingkai
%A Ojha, Varun
%A Liang, Huizhi
%Y Kochmar, Ekaterina
%Y Ghosh, Debanjan
%Y North, Kai
%Y Komachi, Mamoru
%S Proceedings of the 20th International Workshop on Semantic Evaluation (2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-414-9
%F wang-etal-2026-semeval
%X This task introduces Abductive Event Reasoning (AER), a novel shared task, to investigate the ability of Large Language Models(LLMs) to reason about the causality of real-world events. More specifically, a data set consisting of different topics and choices is introduced, and we need to enable the model to select the best options for the given event. Three methods are separately introduced to explore thequestion, including the traditional natural language processing(NLP) method (DeBERTa), theenhanced knowledge graph(KG), and the KG embedded in hyperbolic space.
%U https://aclanthology.org/2026.semeval-1.114/
%P 821-825
Markdown (Informal)
[SemEval-2026 Task 12: Knowledge Graph with hyperbolic embedding in Abductive Event Reasoning](https://aclanthology.org/2026.semeval-1.114/) (Wang et al., SemEval 2026)
ACL