Paper: Disfluency Detection Using Multi-step Stacked Learning

ACL ID N13-1102
Title Disfluency Detection Using Multi-step Stacked Learning
Venue Annual Conference of the North American Chapter of the Association for Computational Linguistics
Session Main Conference
Year 2013

In this paper, we propose a multi-step stacked learning model for disfluency detection. Our method incorporates refined n-gram features step by step from different word sequences. First, we detect filler words. Second, edited words are detected using n-gram features ex- tracted from both the original text and filler fil- tered text. In the third step, additional n-gram features are extracted from edit removed texts together with our newly induced in-between features to improve edited word detection. We useMax-MarginMarkov Networks (M3Ns) as the classifier with the weighted hamming loss to balance precision and recall. Experiments on the Switchboard corpus show that the re- fined n-gram features from multiple steps and M3Ns with weighted hamming loss can signif- icantly improve the performa...