  Compositional vector space models of meaning promise new solutions to
stubborn language understanding problems. This paper makes two contributions
toward this end: (i) it uses automatically-extracted paraphrase examples as a
source of supervision for training compositional models, replacing previous
work which relied on manual annotations used for the same purpose, and (ii)
develops a context-aware model for scoring phrasal compositionality.
Experimental results indicate that these multiple sources of information can be
used to learn partial semantic supervision that matches previous techniques in
intrinsic evaluation tasks. Our approaches are also evaluated for their impact
on a machine translation system where we show improvements in translation
quality, demonstrating that compositionality in interpretation correlates with
compositionality in translation.
