@inproceedings{ou-etal-2020-ynu,
    title = "{YNU}-oxz at {S}em{E}val-2020 Task 5: Detecting Counterfactuals Based on Ordered Neurons {LSTM} and Hierarchical Attention Network",
    author = "Ou, Xiaozhi  and
      Liu, Shengyan  and
      Li, Hongling",
    editor = "Herbelot, Aurelie  and
      Zhu, Xiaodan  and
      Palmer, Alexis  and
      Schneider, Nathan  and
      May, Jonathan  and
      Shutova, Ekaterina",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2020.semeval-1.89/",
    doi = "10.18653/v1/2020.semeval-1.89",
    pages = "683--689",
    abstract = "This paper describes the system and results of our team`s participation in SemEval-2020 Task5: Modelling Causal Reasoning in Language: Detecting Counterfactuals, which aims to simulate counterfactual semantics and reasoning in natural language. This task contains two subtasks: Subtask1{--}Detecting counterfactual statements and Subtask2{--}Detecting antecedent and consequence. We only participated in Subtask1, aiming to determine whether a given sentence is counterfactual. In order to solve this task, we proposed a system based on Ordered Neurons LSTM (ON-LSTM) with Hierarchical Attention Network (HAN) and used Pooling operation for dimensionality reduction. Finally, we used the K-fold approach as the ensemble method. Our model achieved an F1 score of 0.7040 in Subtask1 (Ranked 16/27)."
}