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What is the relation between a word and its description, or a word and itsembedding? Both descriptions and embeddings are semantic representations ofwords. But, what information from the original word remains in theserepresentations? Or more importantly, which information about a word do thesetwo representations share? Definition Modeling and Reverse Dictionary are twoopposite learning tasks that address these questions. The goal of theDefinition Modeling task is to investigate the power of information layinginside a word embedding to express the meaning of the word in a humanlyunderstandable way -- as a dictionary definition. Conversely, the ReverseDictionary task explores the ability to predict word embeddings directly fromits definition. In this paper, by tackling these two tasks, we are exploringthe relationship between words and their semantic representations. We presentour findings based on the descriptive, exploratory, and predictive dataanalysis conducted on the CODWOE dataset. We give a detailed overview of thesystems that we designed for Definition Modeling and Reverse Dictionary tasks,and that achieved top scores on SemEval-2022 CODWOE challenge in severalsubtasks. We hope that our experimental results concerning the predictivemodels and the data analyses we provide will prove useful in futureexplorations of word representations and their relationships.

IRB-NLP at SemEval-2022 Task 1: Exploring the Relationship Between Words and Their Semantic Representations