This paper describes our system for SemEval-2022 Task 2 Multilingual Idiomaticity Detection and Sentence Embedding sub-task B. We modify a standard BERT sentence transformer by adding embeddings for each idioms, which are created using BERTRAM and a small number of contexts. We show that this technique increases the quality of idiom representations and leads to better performance on the task. We also perform analysis on our final results and show that the quality of the produced idiom embeddings is highly sensitive to the quality of the input contexts.
翻译:本文描述了我们的SemEval-2022任务2的系统,即多语种多语种特殊性检测和判决嵌入子任务B。我们修改了标准 BERT 句变压器,为每种特殊性添加嵌入器,这些变压器是使用 BERTRAM 和 少量环境创建的。我们表明,这种技术提高了语言表现的质量,并导致更好地完成任务。我们还分析了我们的最后结果,并表明所制作的idem 嵌入器的质量对于输入环境的质量非常敏感。