Low-Rank Tensors for Verbs in Compositional Distributional Semantics

Several compositional distributional semantic methods use tensors to model multi-way interactions between vectors. Unfortunately, the size of the tensors can make their use impractical in large-scale implementations. In this paper, we investigate whether we can match the performance of full tensors with low-rank approximations that use a fraction of the original number of parameters. We investigate the effect of low-rank tensors on the transitive verb construction where the verb is a third-order tensor. The results show that, while the low-rank tensors require about two orders of magnitude fewer parameters per verb, they achieve performance comparable to, and occasionally surpassing, the unconstrained-rank tensors on sentence similarity and verb disambiguation tasks.

• Type: Journal
• Year: 2015
• Where: ACL
• Authors: Daniel Fried, Tamara Polajnar, Stephen Clark
• Keywords: ACL 2015, Computational Linguistics