Paper: Better Punctuation Prediction with Dynamic Conditional Random Fields

ACL ID D10-1018
Title Better Punctuation Prediction with Dynamic Conditional Random Fields
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2010
Authors

This paper focuses on the task of insert- ing punctuation symbols into transcribed con- versational speech texts, without relying on prosodic cues. We investigate limitations as- sociated with previous methods, and propose a novel approach based on dynamic conditional random fields. Different from previous work, our proposed approach is designed to jointly perform both sentence boundary and sentence type prediction, and punctuation prediction on speech utterances. We performed evaluations on a transcribed conversational speech domain consisting of both English and Chinese texts. Empirical re- sults show that our method outperforms an ap- proach based on linear-chain conditional ran- dom fields and other previous approaches.