Paper: A Beam-Search Decoder for Disfluency Detection

ACL ID C14-1138
Title A Beam-Search Decoder for Disfluency Detection
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2014
Authors

In this paper 1 , we present a novel beam-search decoder for disfluency detection. We first pro- pose node-weighted max-margin Markov networks (M3N) to boost the performance on words belonging to specific part-of-speech (POS) classes. Next, we show the importance of measur- ing the quality of cleaned-up sentences and performing multiple passes of disfluency detection. Finally, we propose using the beam-search decoder to combine multiple discriminative models such as M3N and multiple generative models such as language models (LM) and perform multi- ple passes of disfluency detection. The decoder iteratively generates new hypotheses from current hypotheses by making incremental corrections to the current sentence based on certain patterns as well as information provided by existing models. I...