  Type-based compositional distributional semantic models present an
interesting line of research into functional representations of linguistic
meaning. One of the drawbacks of such models, however, is the lack of training
data required to train each word-type combination. In this paper we address
this by introducing training methods that share parameters between similar
words. We show that these methods enable zero-shot learning for words that have
no training data at all, as well as enabling construction of high-quality
tensors from very few training examples per word.
