Semantic Specialisation of Distributional Word Vector Spaces using Monolingual and Cross-Lingual Constraints
Table 3: Multilingual SimLex-999. The effect of using the Counter-Fitting and Attract-Repel procedures to inject mono- and cross-lingual synonymy and antonymy constraints into the four collections of distributional word vectors. Our best results set the new state-of-the-art performance for all four languages.
['[BOLD] Word Vectors', '[BOLD] English', '[BOLD] German', '[BOLD] Italian', '[BOLD] Russian']
Monolingual specialisation leads to very strong improvements in the SimLex performance across all languages. Cross-lingual specialisation brings further improvements, with all languages benefiting from sharing the cross-lingual vector space. Italian in particular shows strong evidence of effective transfer, with Italian vectors’ performance coming close to the top-performing English ones.