Do Multi-Sense Embeddings Improve Natural Language Understanding?

Learning a distinct representation for each sense of an ambiguous word could lead to more powerful and fine-grained mod-els of vector-space representations. Yet while ‘multi-sense ’ methods have been proposed and tested on artificial word-similarity tasks, we don’t know if they im-prove real natural language understanding tasks. In this paper we introduce a multi-sense embedding model based on Chinese Restaurant Processes that achieves state of the art performance on matching human word similarity judgments, and propose a pipelined architecture for incorporating multi-sense embeddings into language un-derstanding. We then test the performance of our model on part-of-speech tagging, named entity recognition, sentiment analysis, semantic relation identification and semantic relat-edness, controlling for embedding dimen-sionality. We find that multi-sense embed-dings do improve performance on some tasks (part-of-speech tagging, semantic re-lation identification, semantic relatedness) but not on others (named entity recogni-tion, various forms of sentiment analysis). We discuss how these differences may be caused by the different role of word sense information in each of the tasks. The re-sults highlight the importance of testing embedding models in real applications. 1