In particular, we perform an extensive set of experiments on standard benchmarks for bilingual dictionary induction and monolingual and cross-lingual word similarity, as well as on an extrinsic task: cross-lingual hypernym discovery. For both the monolingual and cross-lingual settings, we can notice that our models generally outperform the corresponding baselines.  As can be seen in Table 1 , our refinement method consistently improves over the baselines (i.e., VecMap and MUSE) on all language pairs and metrics. First and foremost, in terms of model-wise comparisons, we observe that our proposed alterations of both VecMap and MUSE improve their quality in a consistent manner, across most metrics and data configurations. 
 Question: What are the tasks that this method has shown improvements?
bilingual dictionary induction, monolingual and cross-lingual word similarity, and cross-lingual hypernym discovery