  While cross-lingual word embeddings have been studied extensively in recent
years, the qualitative differences between the different algorithms remain
vague. We observe that whether or not an algorithm uses a particular feature
set (sentence IDs) accounts for a significant performance gap among these
algorithms. This feature set is also used by traditional alignment algorithms,
such as IBM Model-1, which demonstrate similar performance to state-of-the-art
embedding algorithms on a variety of benchmarks. Overall, we observe that
different algorithmic approaches for utilizing the sentence ID feature space
result in similar performance. This paper draws both empirical and theoretical
parallels between the embedding and alignment literature, and suggests that
adding additional sources of information, which go beyond the traditional
signal of bilingual sentence-aligned corpora, may substantially improve
cross-lingual word embeddings, and that future baselines should at least take
such features into account.
