@inproceedings{vetter-etal-2022-kamikla,
    title = "{K}a{M}i{K}la at {S}em{E}val-2022 Task 3: {A}l{BERT}o, {BERT}, and {C}amem{BERT}{---}{B}e(r)tween Taxonomy Detection and Prediction",
    author = "Vetter, Karl  and
      Segiet, Miriam  and
      Lennermann, Klara",
    editor = "Emerson, Guy  and
      Schluter, Natalie  and
      Stanovsky, Gabriel  and
      Kumar, Ritesh  and
      Palmer, Alexis  and
      Schneider, Nathan  and
      Singh, Siddharth  and
      Ratan, Shyam",
    booktitle = "Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)",
    month = jul,
    year = "2022",
    address = "Seattle, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2022.semeval-1.36/",
    doi = "10.18653/v1/2022.semeval-1.36",
    pages = "282--290",
    abstract = "This paper describes our system submitted for SemEval Task 3: Presupposed Taxonomies: Evaluating Neural Network Semantics (Zamparelli et al., 2022). We participated in both the binary classification and the regression subtask. Target sentences are classified according to their taxonomical relation in subtask 1 and according to their acceptability judgment in subtask 2. Our approach in both subtasks is based on a neural network BERT model. We used separate models for the three languages covered by the task, English, French, and Italian. For the second subtask, we used median averaging to construct an ensemble model. We ranked 15th out of 21 groups for subtask 1 (F1-score: 77.38{\%}) and 11th out of 17 groups for subtask 2 (RHO: 0.078)."
}