Abstract
Spoken utterances do not always abide by linguistically motivated grammatical rules. These utterances exhibit various phenomena considered outside the realm of theoretically-oriented linguistic research. For a language model that extends linguistically motivated grammars with probabilistic reasoning, the problem is how to feature the robustness that is necessary for speech understanding. This paper addresses the issue of the robustness of the Data Oriented Parsing (DOP) model within a Dutch speech-based dialogue system. It presents an extension of the DOP model into a head-driven variant, which allows for Markovian generation of parse trees. It is shown empirically that the new variant improves over the original DOP model on two tasks: the formal understanding of speech utterances, and the extraction of semantic concepts from word lattices output by a speech recognizer.
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Sima’an, K. (2004). Robust Data Oriented Spoken Language Understanding. In: Bunt, H., Carroll, J., Satta, G. (eds) New Developments in Parsing Technology. Text, Speech and Language Technology, vol 23. Springer, Dordrecht. https://doi.org/10.1007/1-4020-2295-6_16
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DOI: https://doi.org/10.1007/1-4020-2295-6_16
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