Bootstrapping Dependency Grammar Inducers from Incomplete Sentence Fragments via Austere Models
Valentin I. Spitkovsky, Hiyan Alshawi, Daniel S. Jurafsky · 2012
Modern grammarinduction systems often employ curriculum learning strategies that begin by training on a subset of all available input that is considered simpler than the full data. Traditionally, filtering has been at granularities of whole input units, e.g., discarding entire sentences with too many words or punctuation marks. We propose instead viewing interpunctuation fragments as atoms, initially, thus making some simple phrases and clauses of complex sentences available to training sooner. Splitting input text at punctuation in this way improved our state-of-the-art grammar induction pipeline. We observe that resulting partial data, i.e., mostly incomplete sentence fragments, can be analyzed using reduced parsing models which, we show, can be easier to bootstrap than more nuanced grammars. Startingwithanew, baredependency-and-boundarymodel(DBM-0), ourgrammarinducer attained 61.2 % directed dependency accuracy on Section 23 (all sentences) of the Wall Street Journal corpus: more than 2 % higher than previous published results for this task.