Using Sentence Type Information for Syntactic Category Acquisition

In this paper we investigate a new source of information for syntactic category acquisition: sentence type (question, declarative, imperative). Sentence type correlates strongly with intonation patterns in most languages; we hypothesize that these intonation patterns are a valuable signal to a language learner, indicating different syntactic patterns. To test this hypothesis, we train a Bayesian Hidden Markov Model (and variants) on child-directed speech. We first show that simply training a separate model for each sentence type decreases performance due to sparse data. As an alternative, we propose two new models based on the BHMM in which sentence type is an observed variable which influences either emission or transition probabilities. Both models outperform a standard BHMM on data from English, Cantonese, and Dutch. This suggests that sentence type information available from intonational cues may be helpful for syntactic acquisition crosslinguistically.