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).- Anthology ID:
- 2020.semeval-1.89
- Volume:
- Proceedings of the Fourteenth Workshop on Semantic Evaluation
- Month:
- December
- Year:
- 2020
- Address:
- Barcelona (online)
- Editors:
- Aurelie Herbelot, Xiaodan Zhu, Alexis Palmer, Nathan Schneider, Jonathan May, Ekaterina Shutova
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- International Committee for Computational Linguistics
- Note:
- Pages:
- 683–689
- Language:
- URL:
- https://aclanthology.org/2020.semeval-1.89
- DOI:
- 10.18653/v1/2020.semeval-1.89
- Cite (ACL):
- Xiaozhi Ou, Shengyan Liu, and Hongling Li. 2020. YNU-oxz at SemEval-2020 Task 5: Detecting Counterfactuals Based on Ordered Neurons LSTM and Hierarchical Attention Network. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 683–689, Barcelona (online). International Committee for Computational Linguistics.
- Cite (Informal):
- YNU-oxz at SemEval-2020 Task 5: Detecting Counterfactuals Based on Ordered Neurons LSTM and Hierarchical Attention Network (Ou et al., SemEval 2020)
- PDF:
- https://preview.aclanthology.org/naacl24-info/2020.semeval-1.89.pdf