In this paper, we describe our method for the detection of lexical semantic
change, i.e., word sense changes over time. We examine semantic differences
between specific words in two corpora, chosen from different time periods, for
English, German, Latin, and Swedish. Our method was created for the SemEval
2020 Task 1: \textit{Unsupervised Lexical Semantic Change Detection.} We ranked
$1^{st}$ in Sub-task 1: binary change detection, and $4^{th}$ in Sub-task 2:
ranked change detection. Our method is fully unsupervised and language
independent. It consists of preparing a semantic vector space for each corpus,
earlier and later; computing a linear transformation between earlier and later
spaces, using Canonical Correlation Analysis and Orthogonal Transformation; and
measuring the cosines between the transformed vector for the target word from
the earlier corpus and the vector for the target word in the later corpus.

本文介绍我们在 SemEval2020 任务 1 中创造性的提出了语义变化检测方法，该方法是全自动的，不需要人工干预，且独立于语言；我们利用标准相关分析和正交变换计算语义空间之间的线性变换，并通过比较早期和晚期语料库中目标单词的向量之间的余弦相似度来检测语义变化。

UWB 在 SemEval-2020 任务 1 中的词汇语义变化检测

UWB at SemEval-2020 Task 1: Lexical Semantic Change Detection

This paper describes our system submitted to task 4 of SemEval 2020:
Commonsense Validation and Explanation (ComVE) which consists of three
sub-tasks. The task is to directly validate the given sentence whether or not
it makes sense and require the model to explain it. Based on BERTarchitecture
with a multi-task setting, we propose an effective and interpretable "Explain,
Reason and Predict" (ERP) system to solve the three sub-tasks about
commonsense: (a) Validation, (b)Reasoning, and (c) Explanation. Inspired by
cognitive studies of common sense, our system first generates a reason or
understanding of the sentences and then chooses which one statement makes
sense, which is achieved by multi-task learning. During the post-evaluation,
our system has reached 92.9% accuracy in subtask A (rank 11), 89.7% accuracy in
subtask B (rank 9), andBLEU score of 12.9 in subtask C (rank 8)

本文提出了一种基于 BERT 模型架构和多任务学习的 “解释，推理和预测” 系统，用于解决 SemEval 2020（通识验证和解释）的子任务，包括通识的验证、推理和解释。该系统受通识认知研究启发，首先对句子的理解和推理做出解释，然后通过多任务学习来选择一条制约语句是否合理，最终在评估中取得了不错的效果。

香港中文大学参加 SemEval-2020 任务 4：基于多任务学习的常识解释、推理和预测

CUHK at SemEval-2020 Task 4: CommonSense Explanation, Reasoning and  Prediction with Multi-task Learning

Propaganda spreads the ideology and beliefs of like-minded people,
brainwashing their audiences, and sometimes leading to violence. SemEval 2020
Task-11 aims to design automated systems for news propaganda detection. Task-11
consists of two sub-tasks, namely, Span Identification - given any news
article, the system tags those specific fragments which contain at least one
propaganda technique; and Technique Classification - correctly classify a given
propagandist statement amongst 14 propaganda techniques. For sub-task 1, we use
contextual embeddings extracted from pre-trained transformer models to
represent the text data at various granularities and propose a
multi-granularity knowledge sharing approach. For sub-task 2, we use an
ensemble of BERT and logistic regression classifiers with linguistic features.
Our results reveal that the linguistic features are the strong indicators for
covering minority classes in a highly imbalanced dataset.

SemEval 2020 Task-11 aims to design automated systems for news propaganda detection, consisting of two sub-tasks, utilizing contextual embeddings and an ensemble of BERT and logistic regression classifiers with linguistic features to identify propaganda techniques and classify propagandist statements respectively.

SemEval-2020 任务 11 中的 BPGC：基于多粒度知识共享和语言特征的集成学习在新闻文章中探测宣传

BPGC at SemEval-2020 Task 11: Propaganda Detection in News Articles with  Multi-Granularity Knowledge Sharing and Linguistic Features based Ensemble  Learning