@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://aclanthology.org/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).",
}
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%0 Conference Proceedings
%T YNU-oxz at SemEval-2020 Task 5: Detecting Counterfactuals Based on Ordered Neurons LSTM and Hierarchical Attention Network
%A Ou, Xiaozhi
%A Liu, Shengyan
%A Li, Hongling
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%Y Palmer, Alexis
%Y Schneider, Nathan
%Y May, Jonathan
%Y Shutova, Ekaterina
%S Proceedings of the Fourteenth Workshop on Semantic Evaluation
%D 2020
%8 December
%I International Committee for Computational Linguistics
%C Barcelona (online)
%F ou-etal-2020-ynu
%X 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).
%R 10.18653/v1/2020.semeval-1.89
%U https://aclanthology.org/2020.semeval-1.89
%U https://doi.org/10.18653/v1/2020.semeval-1.89
%P 683-689
Markdown (Informal)
[YNU-oxz at SemEval-2020 Task 5: Detecting Counterfactuals Based on Ordered Neurons LSTM and Hierarchical Attention Network](https://aclanthology.org/2020.semeval-1.89) (Ou et al., SemEval 2020)
ACL