论文标题
ANA在2020 Semeval-2020任务4：用于常识性推理的多任务学习（UNION）
ANA at SemEval-2020 Task 4: mUlti-task learNIng for cOmmonsense reasoNing (UNION)
论文作者
论文摘要
在本文中，我们描述了针对SEMEVAL2020任务4任务C提交的常识性推理的多任务学习（Union）系统，这是为了产生一个原因，解释了为什么给定的错误陈述为何不敏感。但是，我们在早期的实验中发现，诸如微调GPT2之类的简单适应通常会产生沉闷和非信息的世代（例如，简单的否定）。为了产生更有意义的解释，我们建议联合会（一个统一的端到端框架）利用几个现有的常识数据集，以便它允许模型在常识推理范围内学习更多动态。为了有效，准确，准确，及时执行模型选择，我们还提出了几个辅助自动评估指标，以便我们可以从不同的角度进行广泛比较模型。我们提交的系统不仅可以在拟议的指标中表现出色，而且表现优于其竞争对手，人为评估的得分最高2.10，而BLEU得分保持在15.7。我们的代码在GitHub公开提供。
In this paper, we describe our mUlti-task learNIng for cOmmonsense reasoNing (UNION) system submitted for Task C of the SemEval2020 Task 4, which is to generate a reason explaining why a given false statement is non-sensical. However, we found in the early experiments that simple adaptations such as fine-tuning GPT2 often yield dull and non-informative generations (e.g. simple negations). In order to generate more meaningful explanations, we propose UNION, a unified end-to-end framework, to utilize several existing commonsense datasets so that it allows a model to learn more dynamics under the scope of commonsense reasoning. In order to perform model selection efficiently, accurately and promptly, we also propose a couple of auxiliary automatic evaluation metrics so that we can extensively compare the models from different perspectives. Our submitted system not only results in a good performance in the proposed metrics but also outperforms its competitors with the highest achieved score of 2.10 for human evaluation while remaining a BLEU score of 15.7. Our code is made publicly available at GitHub.
