Semeval-2022 Task 1: CODWOE – Comparing Dictionaries and Word Embeddings

• DOI: 10.18653/v1/2022.semeval-1.1
• Publication Date: 2022-07-26T02:59:46Z
• Authors: Timothee Mickus; Kees Van Deemter; Mathieu Constant; Denis Paperno
• Keywords: FOS: Computer and information sciences; Computer Science - Computation and Language; Languages; NLP; Computation and Language (cs.CL); 01 natural sciences; linguistic studies; 0105 earth and related environmental sciences

Word embeddings have advanced the state of the art in NLP across numerous tasks. Understanding the contents of dense neural representations is of utmost interest to the computational semantics community. We propose to focus on relating these opaque word vectors with human-readable definitions, as found in dictionaries. This problem naturally divides into two subtasks: converting definitions into embeddings, and converting embeddings into definitions. This task was conducted in a multilingual setting, using comparable sets of embeddings trained homogeneously.