@inproceedings{tandon-chatterjee-2023-lrl,
    title = "{LRL}{\_}{NC} at {S}em{E}val-2023 Task 4: The Touche23-{G}eorge-boole Approach for Multi-Label Classification of Human-Values behind Arguments",
    author = "Tandon, Kushagri  and
      Chatterjee, Niladri",
    editor = {Ojha, Atul Kr.  and
      Do{\u{g}}ru{\"o}z, A. Seza  and
      Da San Martino, Giovanni  and
      Tayyar Madabushi, Harish  and
      Kumar, Ritesh  and
      Sartori, Elisa},
    booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2023.semeval-1.19/",
    doi = "10.18653/v1/2023.semeval-1.19",
    pages = "136--142",
    abstract = "The task ValueEval aims at assigning a sub- set of possible human value categories under- lying a given argument. Values behind argu- ments are often determinants to evaluate the relevance and importance of decisions in eth- ical sense, thereby making them essential for argument mining. The work presented here proposes two systems for the same. Both sys- tems use RoBERTa to encode sentences in each document. System1 makes use of features ob- tained from training models for two auxiliary tasks, whereas System2 combines RoBERTa with topic modeling to get sentence represen- tation. These features are used by a classifi- cation head to generate predictions. System1 secured the rank 22 in the official task rank- ing, achieving the macro F1-score 0.46 on the main dataset. System2 was not a part of official evaluation. Subsequent experiments achieved highest (among the proposed systems) macro F1-scores of 0.48 (System2), 0.31 (ablation on System1) and 0.33 (ablation on System1) on the main dataset, the Nahj al-Balagha dataset, and the New York Times dataset."
}