论文标题
Babelencond在Semeval-2020任务3：上下文相似性作为多语言和语言模型的组合
BabelEnconding at SemEval-2020 Task 3: Contextual Similarity as a Combination of Multilingualism and Language Models
论文作者
论文摘要
本文描述了我们的团队（Babelenconding）提交给Semeval-2020任务3：预测上下文在单词相似性中的分级效果的系统。我们提出了一种依赖翻译和多语言语言模型的方法，以计算单词对之间的上下文相似性。我们的假设是，来自其他语言的证据可以利用与人类产生的分数的相关性。 Babelenconding均适用于两个子任务，并在八分之六的任务/语言组合中排名前3位，这是三次得分最高的系统。
This paper describes the system submitted by our team (BabelEnconding) to SemEval-2020 Task 3: Predicting the Graded Effect of Context in Word Similarity. We propose an approach that relies on translation and multilingual language models in order to compute the contextual similarity between pairs of words. Our hypothesis is that evidence from additional languages can leverage the correlation with the human generated scores. BabelEnconding was applied to both subtasks and ranked among the top-3 in six out of eight task/language combinations and was the highest scoring system three times.
