The paper defines weighted head transducers, finite-state machines that perform middle-out string transduction. These transducers are strictly more expressive than the special case of standard left-to-right finite-state transducers. Dependency transduction models are then defined as collections of weighted head transducers that are applied hierarchically. A dynamic programming search algorithm is described for finding the optimal transduction of an input string with respect to a dependency transduction model. A method for automatically training a dependency transduction model from a set of input-output example strings is presented. The method first searches for hierarchical alignments of the training examples guided by correlation statistics, and then constructs the transitions of head transducers that are consistent with these alignments. Experimental results are given for applying the training method to translation from English to Spanish and Japanese.
This paper describes a 'Logical Form' target language for representing the literal meaning of English sentences, and an intermediate level of representation ('Quasi Logical Form') which engenders a natural separation between the compositional semantics and the processes of scoping and reference resolution. The approach has been implemented in the SRI Core Language Engine which handles the English constructions discussed in the paper.
Aspects of semantic interpretation, such as quantifier scoping and reference resolution, are often realised computationally by non-monotonic operations involving loss of information and destructive manipulation of semantic representations. The paper describes how monotonic reference resolution and scoping can be carried out using a revised Quasi Logical Form (QLF) representation. Semantics for QLF are presented in which the denotations of formulas are extended monotonically as QLF expressions are resolved.
This paper describes a method for utterance classification that does not require manual transcription of training data. The method combines domain independent acoustic models with off-the-shelf classifiers to give utterance classification performance that is surprisingly close to what can be achieved using conventional word-trigram recognition requiring manual transcription. In our method, unsupervised training is first used to train a phone n-gram model for a particular domain; the output of recognition with this model is then passed to a phone-string classifier. The classification accuracy of the method is evaluated on three different spoken language system domains.
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