论文标题
IITK-RSA在Semeval-2020任务5：检测反事实
IITK-RSA at SemEval-2020 Task 5: Detecting Counterfactuals
论文作者
论文摘要
本文介绍了我们在解决Semeval-2020的任务5方面的努力。该任务涉及检测一类称为反事实的文本表达式并将其分为组成元素。反事实陈述描述了未发生或无法发生的事件以及此类事件的可能影响。尽管反事实推理对人类是自然的，但由于各种语言的微妙之处，很难理解这些表达方式。我们最终提交的方法是针对第一个子任务的各种基于微型变压器和CNN的各种基于微型变压器和CNN的模型的集合，以及带有第二个子任务的依赖树信息的变压器模型。我们在总排行榜中排名第4和9-排名。我们还探索了涉及使用经典方法，其他神经体系结构以及不同语言特征的各种其他方法。
This paper describes our efforts in tackling Task 5 of SemEval-2020. The task involved detecting a class of textual expressions known as counterfactuals and separating them into their constituent elements. Counterfactual statements describe events that have not or could not have occurred and the possible implications of such events. While counterfactual reasoning is natural for humans, understanding these expressions is difficult for artificial agents due to a variety of linguistic subtleties. Our final submitted approaches were an ensemble of various fine-tuned transformer-based and CNN-based models for the first subtask and a transformer model with dependency tree information for the second subtask. We ranked 4-th and 9-th in the overall leaderboard. We also explored various other approaches that involved the use of classical methods, other neural architectures and the incorporation of different linguistic features.
