@inproceedings{chen-etal-2021-probing,
title = "Probing into the Root: A Dataset for Reason Extraction of Structural Events from Financial Documents",
author = "Chen, Pei and
Liu, Kang and
Chen, Yubo and
Wang, Taifeng and
Zhao, Jun",
booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume",
month = apr,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.eacl-main.175",
doi = "10.18653/v1/2021.eacl-main.175",
pages = "2042--2048",
abstract = "This paper proposes a new task regarding event reason extraction from document-level texts. Unlike the previous causality detection task, we do not assign target events in the text, but only provide structural event descriptions, and such settings accord more with practice scenarios. Moreover, we annotate a large dataset FinReason for evaluation, which provides Reasons annotation for Financial events in company announcements. This task is challenging because the cases of multiple-events, multiple-reasons, and implicit-reasons are included. In total, FinReason contains 8,794 documents, 12,861 financial events and 11,006 reason spans. We also provide the performance of existing canonical methods in event extraction and machine reading comprehension on this task. The results show a 7 percentage point F1 score gap between the best model and human performance, and existing methods are far from resolving this problem.",
}
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%0 Conference Proceedings
%T Probing into the Root: A Dataset for Reason Extraction of Structural Events from Financial Documents
%A Chen, Pei
%A Liu, Kang
%A Chen, Yubo
%A Wang, Taifeng
%A Zhao, Jun
%S Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
%D 2021
%8 apr
%I Association for Computational Linguistics
%C Online
%F chen-etal-2021-probing
%X This paper proposes a new task regarding event reason extraction from document-level texts. Unlike the previous causality detection task, we do not assign target events in the text, but only provide structural event descriptions, and such settings accord more with practice scenarios. Moreover, we annotate a large dataset FinReason for evaluation, which provides Reasons annotation for Financial events in company announcements. This task is challenging because the cases of multiple-events, multiple-reasons, and implicit-reasons are included. In total, FinReason contains 8,794 documents, 12,861 financial events and 11,006 reason spans. We also provide the performance of existing canonical methods in event extraction and machine reading comprehension on this task. The results show a 7 percentage point F1 score gap between the best model and human performance, and existing methods are far from resolving this problem.
%R 10.18653/v1/2021.eacl-main.175
%U https://aclanthology.org/2021.eacl-main.175
%U https://doi.org/10.18653/v1/2021.eacl-main.175
%P 2042-2048
Markdown (Informal)
[Probing into the Root: A Dataset for Reason Extraction of Structural Events from Financial Documents](https://aclanthology.org/2021.eacl-main.175) (Chen et al., EACL 2021)
ACL