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YNU-oxz at SemEval-2020 Task 5: Detecting Counterfactuals Based on Ordered Neurons LSTM and Hierarchical Attention Network

2020-12-01 · SEMEVAL 2020 · Xiaozhi Ou, Shengyan Liu, Hongling Li

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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counterfactualDimensionality ReductionSentence

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Counterfactuals 설명 없음
Tanh Activation 설명 없음
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LSTM An LSTM is a type of recurrent neural network that addresses the vanishing gradient problem in vanilla…

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