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Cross-lingual embeddings represent the meaning of words from differentlanguages in the same vector space. Recent work has shown that it is possibleto construct such representations by aligning independently learned monolingualembedding spaces, and that accurate alignments can be obtained even withoutexternal bilingual data. In this paper we explore a research direction that hasbeen surprisingly neglected in the literature: leveraging noisy user-generatedtext to learn cross-lingual embeddings particularly tailored towards socialmedia applications. While the noisiness and informal nature of the social mediagenre poses additional challenges to cross-lingual embedding methods, we findthat it also provides key opportunities due to the abundance of code-switchingand the existence of a shared vocabulary of emoji and named entities. Ourcontribution consists of a very simple post-processing step that exploits thesephenomena to significantly improve the performance of state-of-the-artalignment methods.

Learning Cross-lingual Embeddings from Twitter via Distant Supervision