  Detecting disfluencies in spontaneous speech is an important preprocessing
step in natural language processing and speech recognition applications.
Existing works for disfluency detection have focused on designing a single
objective only for disfluency detection, while auxiliary objectives utilizing
linguistic information of a word such as named entity or part-of-speech
information can be effective. In this paper, we focus on detecting disfluencies
on spoken transcripts and propose a method utilizing named entity recognition
(NER) and part-of-speech (POS) as auxiliary sequence labeling (SL) tasks for
disfluency detection. First, we investigate cases that utilizing linguistic
information of a word can prevent mispredicting important words and can be
helpful for the correct detection of disfluencies. Second, we show that
training a disfluency detection model with auxiliary SL tasks can improve its
F-score in disfluency detection. Then, we analyze which auxiliary SL tasks are
influential depending on baseline models. Experimental results on the widely
used English Switchboard dataset show that our method outperforms the previous
state-of-the-art in disfluency detection.
