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We introduce Multi-SimLex, a large-scale lexical resource and evaluationbenchmark covering datasets for 12 typologically diverse languages, includingmajor languages (e.g., Mandarin Chinese, Spanish, Russian) as well asless-resourced ones (e.g., Welsh, Kiswahili). Each language dataset isannotated for the lexical relation of semantic similarity and contains 1,888semantically aligned concept pairs, providing a representative coverage of wordclasses (nouns, verbs, adjectives, adverbs), frequency ranks, similarityintervals, lexical fields, and concreteness levels. Additionally, owing to thealignment of concepts across languages, we provide a suite of 66 cross-lingualsemantic similarity datasets. Due to its extensive size and language coverage,Multi-SimLex provides entirely novel opportunities for experimental evaluationand analysis. On its monolingual and cross-lingual benchmarks, we evaluate andanalyze a wide array of recent state-of-the-art monolingual and cross-lingualrepresentation models, including static and contextualized word embeddings(such as fastText, M-BERT and XLM), externally informed lexicalrepresentations, as well as fully unsupervised and (weakly) supervisedcross-lingual word embeddings. We also present a step-by-step dataset creationprotocol for creating consistent, Multi-Simlex-style resources for additionallanguages. We make these contributions -- the public release of Multi-SimLexdatasets, their creation protocol, strong baseline results, and in-depthanalyses which can be be helpful in guiding future developments in multilinguallexical semantics and representation learning -- available via a website whichwill encourage community effort in further expansion of Multi-Simlex to manymore languages. Such a large-scale semantic resource could inspire significantfurther advances in NLP across languages.

Multi-SimLex: A Large-Scale Evaluation of Multilingual and Cross-Lingual Lexical Semantic Similarity