The differences in written text and conversational speech are substantial;
previous parsers trained on treebanked text have given very poor results on
spontaneous speech. For spoken language, the mismatch in style also extends to
prosodic cues, though it is less well understood. This paper re-examines the
use of written text in parsing speech in the context of recent advances in
neural language processing. We show that neural approaches facilitate using
written text to improve parsing of spontaneous speech, and that prosody further
improves over this state-of-the-art result. Further, we find an asymmetric
degradation from read vs. spontaneous mismatch, with spontaneous speech more
generally useful for training parsers.

本文通过深度学习技术重新审视了语言处理中书面文本对口语处理的作用，发现书面文本与口语数据的不匹配使得基于书面文本训练的处理器应用于口语处理时表现不佳，而基于神经网络的方法使书面文本可以用于提高口语处理性能，同时考虑语调因素可以进一步提高性能，特别是对于模型训练而言，自然语言生成的语音数据更具有实际应用价值。