While cross-lingual word embeddings have been studied extensively in recent years, the qualitative differences between the different algorithms remains 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, is an appealing approach for substantially improving crosslingual word embeddings.

该论文探讨了跨语言词嵌入在算法使用不同特征集合时的表现差异，并发现使用句子ID特征集合的算法表现显著优于不使用的算法。作者认为利用句子ID特征集合的算法与传统对齐算法在性能表现上较为相似，而通过将额外的信息引入跨语言词嵌入，这种性能差异可能得到进一步改善。

从句子对齐学习跨语言词向量的强基线