Spoken dialogue grammar induction from crowdsourced data

We design and evaluate various crowdsourcing tasks for eliciting spoken dialogue data. Task design is based on an array of parameters that quantify the basic characteristics of the elicitation questions, e.g., how open-ended is a question. The crowdsourced data are used for and evaluated on the unsupervised induction of semantic classes for speech understanding grammars. We show that grammar induction performance is significantly affected by the crowdsourcing task parameters, e.g., paraphrasing tasks prime high lexical entrain-ment and result in poor corpus/grammar quality. The task parameters along with perplexity filters are used for corpus selection achieving grammar induction performance that is comparable to that of using in-domain spoken dialogue data.