  We propose a Bayesian model of unsupervised semantic role induction in
multiple languages, and use it to explore the usefulness of parallel corpora
for this task. Our joint Bayesian model consists of individual models for each
language plus additional latent variables that capture alignments between roles
across languages. Because it is a generative Bayesian model, we can do
evaluations in a variety of scenarios just by varying the inference procedure,
without changing the model, thereby comparing the scenarios directly. We
compare using only monolingual data, using a parallel corpus, using a parallel
corpus with annotations in the other language, and using small amounts of
annotation in the target language. We find that the biggest impact of adding a
parallel corpus to training is actually the increase in mono-lingual data, with
the alignments to another language resulting in small improvements, even with
labeled data for the other language.
