In this paper 1 , we present a novel beam-search decoder for disfluency detection. We first propose 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 measuring 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 multiple 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. It then rescores each hypothesis based on features of lexical correctness and fluency. Our decoder achieves an edit-word F1 score higher than all previous published scores on the same data set, both with and without using external sources of information.
Following this work, REF used a beam-search decoder to combine multiple models such as M 3 N and language model, they achieved the highest f-score.
A Beam-Search Decoder for Disfluency Detection
COLING
Computer Science