This paper describes work of the BUT-FIT's team at SemEval 2020 Task 4 -
Commonsense Validation and Explanation. We participated in all three subtasks.
In subtasks A and B, our submissions are based on pretrained language
representation models (namely ALBERT) and data augmentation. We experimented
with solving the task for another language, Czech, by means of multilingual
models and machine translated dataset, or translated model inputs. We show that
with a strong machine translation system, our system can be used in another
language with a small accuracy loss. In subtask C, our submission, which is
based on pretrained sequence-to-sequence model (BART), ranked 1st in BLEU score
ranking, however, we show that the correlation between BLEU and human
evaluation, in which our submission ended up 4th, is low. We analyse the
metrics used in the evaluation and we propose an additional score based on
model from subtask B, which correlates well with our manual ranking, as well as
reranking method based on the same principle. We performed an error and dataset
analysis for all subtasks and we present our findings.

本文描述了 BUT-FIT 团队在 SemEval 2020 任务 4（常识验证和解释）中的工作，涉及三个子任务：A、B、C。其中，在子任务 A 和 B 中，我们的提交基于预训练语言表示模型和数据增强。我们尝试了使用多语言模型和机器翻译数据集或翻译模型输入来解决另一种语言（捷克语）的任务。在子任务 C 中，我们的提交基于预训练序列到序列模型，排名第一。我们还对所有子任务进行了误差和数据集分析，并提出了我们的发现。