Large-Scale Syntactic Language Modeling with Treelets

We propose a simple generative, syntactic language model that conditions on overlapping windows of tree context (or treelets) in the same way that n-gram language models condition on overlapping windows of linear context. We estimate the parameters of our model by collecting counts from automatically parsed text using standard n-gram language model estimation techniques, allowing us to train a model on over one billion tokens of data using a single machine in a matter of hours. We evaluate on perplexity and a range of grammaticality tasks, and find that we perform as well or better than n-gram models and other generative baselines. Our model even competes with state-of-the-art discriminative models hand-designed for the grammaticality tasks, despite training on positive data alone. We also show fluency improvements in a preliminary machine translation experiment.

• Type: Journal
• Year: 2012
• Where: ACL
• Authors: Adam Pauls, Dan Klein
• Keywords: ACL 2012, Computational Linguistics, Estimation Techniques, Linear Context, Model Estimation