This paper describes our system for the SemEval2022 task of matching dictionary glosses to word embeddings. We focus on the Reverse Dictionary Track of the competition, which maps multilingual glosses to reconstructed vector representations. More specifically, models convert the input of sentences to three types of embeddings: SGNS, Char, and Electra. We propose several experiments for applying neural network cells, general multilingual and multitask structures, and language-agnostic tricks to the task. We also provide comparisons over different types of word embeddings and ablation studies to suggest helpful strategies. Our initial transformer-based model achieves relatively low performance. However, trials on different retokenization methodologies indicate improved performance. Our proposed Elmobased monolingual model achieves the highest outcome, and its multitask, and multilingual varieties show competitive results as well.

本文介绍了我们针对SemEval2022词典义项匹配任务所设计的系统，主要集中在比赛的Reverse Dictionary Track上，涉及到多语言义项到重构向量的映射，主要是将输入句子转换为三类不同类型的嵌入。我们提出了几个实验，包括神经网络单元、通用多语言和多任务结构，以及语言无关技巧。最终我们的Elmobased单语言模型表现最佳，并且多任务、多语言变体也表现出竞争力。

1Cademy at Semeval-2022 Task 1: Investigating the Effectiveness of
  Multilingual, Multitask, and Language-Agnostic Tricks for the Reverse
  Dictionary Task

在Semeval-2022任务1中的1Cademy：通过多语言、多任务和语言无关技巧，研究反向词典任务的有效性