@inproceedings{cignarella-etal-2022-dependency,
    title = "Do Dependency Relations Help in the Task of Stance Detection?",
    author = "Cignarella, Alessandra Teresa  and
      Bosco, Cristina  and
      Rosso, Paolo",
    editor = "Tafreshi, Shabnam  and
      Sedoc, Jo{\~a}o  and
      Rogers, Anna  and
      Drozd, Aleksandr  and
      Rumshisky, Anna  and
      Akula, Arjun",
    booktitle = "Proceedings of the Third Workshop on Insights from Negative Results in NLP",
    month = may,
    year = "2022",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2022.insights-1.2/",
    doi = "10.18653/v1/2022.insights-1.2",
    pages = "10--17",
    abstract = "In this paper we present a set of multilingual experiments tackling the task of Stance Detection in five different languages: English, Spanish, Catalan, French and Italian. Furthermore, we study the phenomenon of stance with respect to six different targets {--} one per language, and two different for Italian {--} employing a variety of machine learning algorithms that primarily exploit morphological and syntactic knowledge as features, represented throughout the format of Universal Dependencies. Results seem to suggest that the methodology employed is not beneficial per se, but might be useful to exploit the same features with a different methodology."
}