论文标题
KALM在Semeval-2020任务4：理解和发电的知识意识语言模型
KaLM at SemEval-2020 Task 4: Knowledge-aware Language Models for Comprehension And Generation
论文作者
论文摘要
本文介绍了我们在Semeval 2020任务4：常识验证和解释中的策略。我们提出了一种搜索证据的新方法，并选择不同的大规模预训练模型作为三个子任务的骨干。结果表明，我们的证据搜索方法改善了常识性解释任务的模型绩效。根据人类评估得分，我们的团队在子任务C中排名第二。
This paper presents our strategies in SemEval 2020 Task 4: Commonsense Validation and Explanation. We propose a novel way to search for evidence and choose the different large-scale pre-trained models as the backbone for three subtasks. The results show that our evidence-searching approach improves model performance on commonsense explanation task. Our team ranks 2nd in subtask C according to human evaluation score.
