Do Multi-Sense Embeddings Improve Natural Language Understanding?
Table 1: Performances for different set of multi-sense embeddings (300d) evaluated on SCWS by measuring the Spearman correlation between each model’s similarity and the human judgments.
['Model', 'Dataset', 'SCWS Correlation']
Consistent with earlier work (e.g.., \newciteneelakantan2014efficient), we find that multi-sense embeddings result in better performance in the context-dependent SCWS task (SG+Greedy and SG+Expect are better than SG). As expected, performance is not as high when global level information is ignored when choosing word senses (SG+Greedy) as when it is included (SG+Expect), as neighboring words don’t provide sufficient information for word sense disambiguation. SG+Expect yields +2.4