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.

研究跨语言词嵌入的双语词典归纳的任务，发现现有数据集存在的困扰性问题：（1）数据中有 1/4 的专有名词，难以反映 BDI 表现，（2）黄金标准数据存在普遍间隔，对个体语言之间交叉嵌入系统排名和性能差异的总体度的影响，提出未来的研究要么避免从此 BDI 数据集中定量结果中得出结论，要么伴随严格的误差分析的评估。