  Most of recent work in cross-lingual word embeddings is severely
Anglocentric. The vast majority of lexicon induction evaluation dictionaries
are between English and another language, and the English embedding space is
selected by default as the hub when learning in a multilingual setting. With
this work, however, we challenge these practices. First, we show that the
choice of hub language can significantly impact downstream lexicon induction
performance. Second, we both expand the current evaluation dictionary
collection to include all language pairs using triangulation, and also create
new dictionaries for under-represented languages. Evaluating established
methods over all these language pairs sheds light into their suitability and
presents new challenges for the field. Finally, in our analysis we identify
general guidelines for strong cross-lingual embeddings baselines, based on more
than just Anglocentric experiments.
