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In this paper, we introduce MAMUS for constructing multilingual sparse word representations. Our algorithm operates by determining a shared set of semantic units which get reutilized across languages, providing it a competitive edge both in terms of speed and evaluation performance. We demonstrate that our proposed algorithm behaves competitively to strong baselines through a series of rigorous experiments performed towards downstream applications spanning over dependency parsing, documentdblp:conf/iclr/Berend20 fatcat:zoommhwkq5db7dqw6l4mk6v2ui