  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.
