Word embeddings, which represent a word as a point in a vector space, have
become ubiquitous to several NLP tasks. A recent line of work uses bilingual
(two languages) corpora to learn a different vector for each sense of a word,
by exploiting crosslingual signals to aid sense identification. We present a
multi-view Bayesian non-parametric algorithm which improves multi-sense word
embeddings by (a) using multilingual (i.e., more than two languages) corpora to
significantly improve sense embeddings beyond what one achieves with bilingual
information, and (b) uses a principled approach to learn a variable number of
senses per word, in a data-driven manner. Ours is the first approach with the
ability to leverage multilingual corpora efficiently for multi-sense
representation learning. Experiments show that multilingual training
significantly improves performance over monolingual and bilingual training, by
allowing us to combine different parallel corpora to leverage multilingual
context. Multilingual training yields comparable performance to a state of the
art mono-lingual model trained on five times more training data.

该论文介绍了一种利用多语种语料库改进多义词嵌入的多视角贝叶斯非参数算法，这是第一个能有效利用多语种语料库进行多意词表示学习的方法，结果表明采用多语种语料库可显著提高性能。