We introduce a new approach for disfluency detection using a Bidirectional
Long-Short Term Memory neural network (BLSTM). In addition to the word
sequence, the model takes as input pattern match features that were developed
to reduce sensitivity to vocabulary size in training, which lead to improved
performance over the word sequence alone. The BLSTM takes advantage of explicit
repair states in addition to the standard reparandum states. The final output
leverages integer linear programming to incorporate constraints of disfluency
structure. In experiments on the Switchboard corpus, the model achieves
state-of-the-art performance for both the standard disfluency detection task
and the correction detection task. Analysis shows that the model has better
detection of non-repetition disfluencies, which tend to be much harder to
detect.

本文提出了一种利用双向 LSTM 神经网络进行自修复检测的方法，并使用模式匹配特征来降低对于词汇量的敏感度，提高了模型的性能。实验结果表明，在 Switchboard 语料库中，模型在标准的自修复检测任务和纠正检测任务中均表现出了出色的性能，特别是将非重复性的自修复鉴别效果提升明显。