Spoken Dialogue Interpretation with the DOP Model

Spoken Dialogue Interpretation with the DOP Model Rens Bod Department of Computational Linguistics University of Amsterdam Spuistraat 134, 1012 VB Amsterdam rens.bod @ let.uva.nl Abstract We show how the DOP model can be used for fast and robust processing of spoken input in a practical spoken dialogue system called OVIS. OVIS, Openbaar Vervoer Informatie Systeem ("Public Transport Infor- mation System"), is a Dutch spoken language infor- mation system which operates over ordinary telephone lines. The prototype system is the immediate goal of the NWO 1 Priority Programme "Language and Speech Technology". In this paper, we extend the original DOP model to context-sensitive interpretation of spoken input. The system we describe uses the OVIS corpus (10,000 trees enriched with compositional semantics) to compute from an input word-graph the best utterance together with its meaning. Dialogue context is taken into account by dividing up the OVIS corpus into context-dependent subcorpora. Each system question triggers a subcorpus by which the user answer is analyzed and interpreted. Our experiments indicate that the context-sensitive DOP model obtains better accuracy than the original model, allowing for fast and robust processing of spoken input. 1. Introduction The Data-Oriented Parsing (DOP) model (cf. Bod 1992, 1995; Bod & Kaplan 1998; Scha 1992; Sima'an 1995, 1997; Rajman 1995) is a probabilistic parsing model which does not single out a narrowly predefined set of structures as the statistically significant ones. It accomplishes this by maintaining a large corpus of analyses of previously occurring utterances. New utterances are analyzed by combining subtrees from the corpus. The occurrence-frequencies of the subtrees are used to estimate the most probable analysis of an utterance. To date, DOP has mainly been applied to corpora of trees labeled with syntactic annotations. Let us illustrate this with a very simple example. Suppose that a corpus consists of only two trees: (1) S S NF' VP NP VP John V NP Peter V NP I I I I likes Mary hates Susan I Netherlands Organization for Scientific Research 138 To combine subtrees, a node-substitution operation indicated as o is used. Node-substitution identifies the leftmost nonterminal frontier node of one tree with the root node of a second tree (i.e., the second tree is substituted on the leftmost nonterminal frontier node of the first tree). A new input sentence such as Mar3' likes Susan can thus be parsed by combining subtrees from this corpus, as in (2): (2) S o NP o NP = S NP Vl' Mary Susan NP VP V NP Mary V NP I I I likes likes Susan Other derivations may yield the same parse tree; for instance: (3) S o NP o V = S NP VP Mary likes NP VP V NP Mary V NP I I I Susan likes Susan DOP computes the probability of substituting a subtree t on a specific node as the probability of selecting t among all subtrees in the corpus that could be substituted on that node. This probability is equal to the number of occurrences of …

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