In contemporary machine learning approaches to bilingual lexicon induction
(BLI), a model learns a mapping between the embedding spaces of a language
pair. Recently, retrieve-and-rank approach to BLI has achieved state of the art
results on the task. However, the problem remains challenging in low-resource
settings, due to the paucity of data. The task is complicated by factors such
as lexical variation across languages. We argue that the incorporation of
additional lexical information into the recent retrieve-and-rank approach
should improve lexicon induction. We demonstrate the efficacy of our proposed
approach on XLING, improving over the previous state of the art by an average
of 2\% across all language pairs.

在现代机器学习中，双语词汇诱导（BLI）的方法中，模型学习了语言对的嵌入空间之间的映射。最近，BLI 的检索与排序方法在该任务上取得了最先进的结果。然而，在资源匮乏的环境下，该问题仍然具有挑战性，因为数据非常稀缺。跨语言的词汇变异等因素使得该任务变得复杂。我们认为将额外的词汇信息纳入最新的检索与排序方法应该能够改善词汇诱导。我们在 XLING 上展示了我们提出方法的功效，在所有语言对中，平均提升了 2% 以上的性能，超过了先前的最佳结果。