Budget Argument Mining Dataset Using Japanese Minutes from the National Diet and Local Assemblies

Yasutomo Kimura, Hokuto Ototake, Minoru Sasaki


Abstract
Budget argument mining attempts to identify argumentative components related to a budget item, and then classifies these argumentative components, given budget information and minutes. We describe the construction of the dataset for budget argument mining, a subtask of QA Lab-PoliInfo-3 in NTCIR-16. Budget argument mining analyses the argument structure of the minutes, focusing on monetary expressions (amount of money). In this task, given sufficient budget information (budget item, budget amount, etc.), relevant argumentative components in the minutes are identified and argument labels (claim, premise, and other) are assigned their components. In this paper, we describe the design of the data format, the annotation procedure, and release information of budget argument mining dataset, to link budget information to minutes.
Anthology ID:
2022.lrec-1.659
Volume:
Proceedings of the Thirteenth Language Resources and Evaluation Conference
Month:
June
Year:
2022
Address:
Marseille, France
Editors:
Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Jan Odijk, Stelios Piperidis
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
6131–6138
Language:
URL:
https://aclanthology.org/2022.lrec-1.659
DOI:
Bibkey:
Cite (ACL):
Yasutomo Kimura, Hokuto Ototake, and Minoru Sasaki. 2022. Budget Argument Mining Dataset Using Japanese Minutes from the National Diet and Local Assemblies. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 6131–6138, Marseille, France. European Language Resources Association.
Cite (Informal):
Budget Argument Mining Dataset Using Japanese Minutes from the National Diet and Local Assemblies (Kimura et al., LREC 2022)
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PDF:
https://preview.aclanthology.org/nschneid-patch-1/2022.lrec-1.659.pdf