Automatic speech recognition (ASR) outputs often contain various disfluencies. It is necessary to remove these disfluencies before processing downstream tasks. In this paper, an efficient disfluency detection approach based on right-to-left transition-based parsing is proposed, which can efficiently identify disfluencies and keep ASR outputs grammatical. Our method exploits a global view to capture long-range dependencies for disfluency detection by integrating a rich set of syntactic and disfluency features with linear complexity. The experimental results show that our method outperforms state-of-the-art work and achieves a 85.1\% f-score on the commonly used English Switchboard test set. We also apply our method to in-house annotated Chinese data and achieve a significantly higher f-score compared to the baseline of CRF-based approach.