@inproceedings{akiba-etal-2024-masking,
    title = "Masking Explicit Pro-Con Expressions for Development of a Stance Classification Dataset on Assembly Minutes",
    author = "Akiba, Tomoyosi  and
      Gato, Yuki  and
      Kimura, Yasutomo  and
      Uchida, Yuzu  and
      Takamaru, Keiichi",
    editor = "Afli, Haithem  and
      Bouamor, Houda  and
      Casagran, Cristina Blasi  and
      Ghannay, Sahar",
    booktitle = "Proceedings of the Second Workshop on Natural Language Processing for Political Sciences @ LREC-COLING 2024",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.politicalnlp-1.4",
    pages = "33--38",
    abstract = "In this paper, a new dataset for Stance Classification based on assembly minutes is introduced. We develop it by using publicity available minutes taken from diverse Japanese local governments including prefectural, city, and town assemblies. In order to make the task to predict a stance from content of a politician{'}s utterance without explicit stance expressions, predefined words that directly convey the speaker{'}s stance in the utterance are replaced by a special token. Those masked words are also used to assign a golden label, either agreement or disagreement, to the utterance. Finally, we constructed total 15,018 instances automatically from 47 Japanese local governments. The dataset is used in the shared Stance Classification task evaluated in the NTCIR-17 QA-Lab-PoliInfo-4, and is now publicity available. Since the construction method of the dataset is automatic, we can still apply it to obtain more instances from the other Japanese local governments.",
}

In this paper, a new dataset for Stance Classification based on assembly minutes is introduced. We develop it by using publicity available minutes taken from diverse Japanese local governments including prefectural, city, and town assemblies. In order to make the task to predict a stance from content of a politician{'}s utterance without explicit stance expressions, predefined words that directly convey the speaker{'}s stance in the utterance are replaced by a special token. Those masked words are also used to assign a golden label, either agreement or disagreement, to the utterance. Finally, we constructed total 15,018 instances automatically from 47 Japanese local governments. The dataset is used in the shared Stance Classification task evaluated in the NTCIR-17 QA-Lab-PoliInfo-4, and is now publicity available. Since the construction method of the dataset is automatic, we can still apply it to obtain more instances from the other Japanese local governments.
Akiba, Tomoyosi Gato, Yuki Kimura, Yasutomo Uchida, Yuzu Takamaru, Keiichi
Masking Explicit Pro-Con Expressions for Development of a Stance Classification Dataset on Assembly Minutes