Massively Multilingual Word Embeddings
Carnegie Mellon University（卡内基梅隆大学）；University of Washington（华盛顿大学）
英文摘要：
We introduce new methods for estimating and evaluating embeddings of words in more than fifty languages in a single shared embedding space. Our estimation methods, multiCluster and multiCCA, use dictionaries and monolingual data; they do not require parallel data. Our new evaluation method, multiQVEC-CCA, is shown to correlate better than previous ones with two downstream tasks (text categorization and parsing). We also describe a web portal for evaluation that will facilitate further research in this area, along with open-source releases of all our methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
