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Although disfluent speech is pervasive in spoken conversation, disfluencies have received little attention within formal
theories of grammar---they are widely perceived as meaning-free production errors. The majority of work on disfluent language has
come from psycholinguistic models of speech production and comprehension and from structural approaches designed to
improve performance in speech applications. Over recent years much evidence has accumulated that disfluencies, far
from being meaningless noise, contain much useful information that guides language users's actions and evaluations of
their interlocuters' states of mind. Moreover, they exhibit rule-like regularities on all levels (including phonology, syntax,
and semantics.).  In DUEL we aim to show how disfluent speech across a number of languages (including
French, German, English, and Chinese) can be analyzed in a precise way on the basis of formal grammatical tools,  using
this theory to guide the design of dialogue systems which can deal head on with disfluent speech, exploiting the information
therein rather than  filtering it away.  DUEL will also tackle another phenomenon that has not hitherto received attention
from formal grammarians, namely laughter. Empirical studies over recent years have shown that these occur relatively
frequently in conversation and do not typically involve `humorous' utterances. They play a significant semantic role, e.g. in
indicating an utterance is not to be taken seriously or in enabling a socially delicate utterance to be made without causing
offence. Our aim is to develop precise analyses of  how laughter is integrated in the emergence of meaning, precise
enough to enable dialogue systems that understand and respond to laughter to be implemented. The tools developed in
DUEL to analyze disfluency and laughter will  enable a variety of other dialogical phenomena that have been somewhat
marginal to be analyzed, e,g, exclamations, tag questions, and corrective particles such as `No'. Both theory and
implementation in DUEL will draw on carefully collected parallel data in French, German, and Chinese, as well as a small
number of experimental studies. This will  enable subtle cross-linguistic
and cross-cultural differences to be described, as well as deeper commonalities to be hypothesized.