Recent work on bilingual lexicon induction (BLI) has frequently depended
either on aligned bilingual lexicons or on distribution matching, often with an
assumption about the isometry of the two spaces. We propose a technique to
quantitatively estimate this assumption of the isometry between two embedding
spaces and empirically show that this assumption weakens as the languages in
question become increasingly etymologically distant. We then propose Bilingual
Lexicon Induction with Semi-Supervision (BLISS) --- a semi-supervised approach
that relaxes the isometric assumption while leveraging both limited aligned
bilingual lexicons and a larger set of unaligned word embeddings, as well as a
novel hubness filtering technique. Our proposed method obtains state of the art
results on 15 of 18 language pairs on the MUSE dataset, and does particularly
well when the embedding spaces don't appear to be isometric. In addition, we
also show that adding supervision stabilizes the learning procedure, and is
effective even with minimal supervision.

该论文提出了一种半监督的双语词汇识别方法（BLISS），该方法使用了一个新的中枢过滤技术，可以放松两个嵌入空间同构假设，利用有限的对齐双语词汇表和更大的未对齐词汇表，实现了令人瞩目的效果。