  The task of bilingual dictionary induction (BDI) is commonly used for
intrinsic evaluation of cross-lingual word embeddings. The largest dataset for
BDI was generated automatically, so its quality is dubious. We study the
composition and quality of the test sets for five diverse languages from this
dataset, with concerning findings: (1) a quarter of the data consists of proper
nouns, which can be hardly indicative of BDI performance, and (2) there are
pervasive gaps in the gold-standard targets. These issues appear to affect the
ranking between cross-lingual embedding systems on individual languages, and
the overall degree to which the systems differ in performance. With proper
nouns removed from the data, the margin between the top two systems included in
the study grows from 3.4% to 17.2%. Manual verification of the predictions, on
the other hand, reveals that gaps in the gold standard targets artificially
inflate the margin between the two systems on English to Bulgarian BDI from
0.1% to 6.7%. We thus suggest that future research either avoids drawing
conclusions from quantitative results on this BDI dataset, or accompanies such
evaluation with rigorous error analysis.
