  Cross-lingual embeddings represent the meaning of words from different
languages in the same vector space. Recent work has shown that it is possible
to construct such representations by aligning independently learned monolingual
embedding spaces, and that accurate alignments can be obtained even without
external bilingual data. In this paper we explore a research direction that has
been surprisingly neglected in the literature: leveraging noisy user-generated
text to learn cross-lingual embeddings particularly tailored towards social
media applications. While the noisiness and informal nature of the social media
genre poses additional challenges to cross-lingual embedding methods, we find
that it also provides key opportunities due to the abundance of code-switching
and the existence of a shared vocabulary of emoji and named entities. Our
contribution consists of a very simple post-processing step that exploits these
phenomena to significantly improve the performance of state-of-the-art
alignment methods.
