Export Reviews, Discussions, Author Feedback and Meta-Reviews

"NIPS Neural Information Processing Systems 8-11th December 2014, Montreal, Canada",,, "Paper ID:","1233" "Title:","A Multiplicative Model for Learning Distributed Text-Based Attribute Representations" Current Reviews First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. This paper proposes to incorporate side information for improving vector-space embedding of words via an attribute vector that modulates the word-projection matrices. One could simply think of word-projection tensors (although, in practice the tensors are factorized) where the attribute vector provide the loadings for the tensor slices. This is studied in the context of log-bilinear language models, but the basic idea should be applicable to other word embedding work. The theory part of the paper is very well-written. However, it is in the experimental section that things get somewhat muddier.