Comparison of Semantic Similarity for Different Languages Using the Google n-gram Corpus and Second-Order Co-occurrence Measures

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A Survey of Cross-lingual Word Embedding Models (2017, background): “Extending them to the multilingual setting then mainly involves translating the word pairs into different languages: WordSim-353 has been translated to Spanish, Romanian, and Arabic (Hassan & Mihalcea, 2009) and to German, Italian, and Russian (Leviant & Reichart, 2015); RG was translated to German ”
Unsupervised Morphology Induction Using Word Embeddings (2015, methodology): “For French we use the RG65 French version (Joubarne and Inkpen, 2011); for Spanish, Romanian, and Arabic we use their respective versions of WS353 (Hassan and Mihalcea, 2009).”
SemEval-2017 Task 2: Multilingual and Cross-lingual Semantic Word Similarity (2017, background): “, 2002) datasets have been translated into other languages, either by experts (Gurevych, 2005; Joubarne and Inkpen, 2011; Granada et al., 2014; CamachoCollados et al., 2015), or by means of crowdsourcing (Leviant and Reichart, 2015), thereby creating equivalent datasets in languages other than Engli”