Combining semantic and syntactic structure for language modeling

Structured language models for speech recognition have been shown to remedy the weaknesses of n-gram models. All current structured language models are, however, limited in that they do not take into account dependencies between non-headwords. We show that non-headword dependencies contribute to significantly improved word error rate, and that a data-oriented parsing model trained on semantically and syntactically annotated data can exploit these dependencies. This paper also contains the first DOP model trained by means of a maximum likelihood reestimation procedure, which solves some of the theoretical shortcomings of previous DOP models.Comment: 4 page

From Monologue to Dialogue: Natural Language Generation in OVIS

This paper describes how a language generation system that was originally designed for monologue generation, has been adapted for use in the OVIS spoken dialogue system. To meet the requirement that in a dialogue, the system's utterances should make up a single, coherent dialogue turn, several modifications had to be made to the system. The paper also discusses the influence of dialogue context on information status, and its consequences for the generation of referring expressions and accentuation

Spoken Dialogue Interpretation with the DOP Model

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 Information System"), is a Dutch spoken language information 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

Spoken Dialogue Interpretation with the DOP Model

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