Combining Punctuation and Disfluency Prediction: An Empirical Study

Punctuation prediction and disfluency pre-diction can improve downstream natural language processing tasks such as ma-chine translation and information extrac-tion. Combining the two tasks can poten-tially improve the efficiency of the over-all pipeline system and reduce error prop-agation. In this work1, we compare var-ious methods for combining punctuation prediction (PU) and disfluency prediction (DF) on the Switchboard corpus. We com-pare an isolated prediction approach with a cascade approach, a rescoring approach, and three joint model approaches. For the cascade approach, we show that the soft cascade method is better than the hard cascade method. We also use the cas-cade models to generate an n-best list, use the bi-directional cascade models to per-form rescoring, and compare that with the results of the cascade models. For the joint model approach, we compare mixed-label Linear-chain Conditional Random Field (LCRF), cross-product LCRF and 2