@InProceedings{ghaly-mandel:2017:Speech-Centric,
  author    = {Ghaly, Hussein  and  Mandel, Michael},
  title     = {Analyzing Human and Machine Performance In Resolving Ambiguous Spoken Sentences},
  booktitle = {Proceedings of the Workshop on Speech-Centric Natural Language Processing},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {18--26},
  abstract  = {Written sentences can be more ambiguous than spoken sentences. We investigate
	this difference for two different types of ambiguity: prepositional phrase (PP)
	attachment and sentences where the addition of commas changes the meaning. We
	recorded a native English speaker saying several of each type of sentence both
	with and without disambiguating contextual information.  These sentences were
	then presented either as text or audio and either with or without context to
	subjects who were asked to select the proper interpretation of the sentence.
	Results suggest that comma-ambiguous sentences are easier to disambiguate than
	PP-attachment-ambiguous sentences, possibly due to the presence of clear
	prosodic boundaries, namely silent pauses. Subject performance for sentences
	with PP-attachment ambiguity without context was 52% for text only while it was
	72.4% for audio only, suggesting that audio has more disambiguating information
	than text. Using an analysis of acoustic features of two PP-attachment
	sentences, a simple classifier was implemented to resolve the PP-attachment
	ambiguity being early or late closure with a mean accuracy of 80%.},
  url       = {http://www.aclweb.org/anthology/W17-4603}
}