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。

BUT-FIT 参加 SemEval-2020 任务 4：多语言常识

BUT-FIT at SemEval-2020 Task 4: Multilingual commonsense

Many natural language processing (NLP) tasks involve reasoning with textual
spans, including question answering, entity recognition, and coreference
resolution. While extensive research has focused on functional architectures
for representing words and sentences, there is less work on representing
arbitrary spans of text within sentences. In this paper, we conduct a
comprehensive empirical evaluation of six span representation methods using
eight pretrained language representation models across six tasks, including two
tasks that we introduce. We find that, although some simple span
representations are fairly reliable across tasks, in general the optimal span
representation varies by task, and can also vary within different facets of
individual tasks. We also find that the choice of span representation has a
bigger impact with a fixed pretrained encoder than with a fine-tuned encoder.

本文通过对六种跨度表示方法和八个预训练语言模型在六个任务中的综合实证评估，发现即使有些简单跨度表示在多个任务中相对可靠，但一般来说最优跨度表示方法因任务而异，也可能因不同任务的不同方面而异。同时，我们还发现，与精细调整的编码器相比，跨度表示的选择对于预先训练的编码器具有更大的影响。