We propose an architecture for integrating discourse processing and speech recognition (SR) in spoken dialogue systems. It was first developed for computer-mediated bilingual dialogue in voiceto -voice machine translation applications and we apply it here to a distributed battlefield simulation system used for military training. According to this architecture discourse functions previously distributed through the interface code are collected into a centra lized discourse capability. The Dialogue Manager (DM) acts as a third-party mediator overseeing the translation of input and output utterances between English and the command language of the backend system. The DM calls the Discourse Processor (DP) to update the context representation each time an utterance is issued or when a salient non-linguistic event occurs in the simulation. The DM is responsible for managing the interaction among components of the interface system and the user. For task-based human -computer dialogue systems it consults three sources of nonlinguistic context constraint in addition to the linguistic Discourse State: (1) a User Model, (2) a static Domain Model containing rules for engaging the backend system, with a grammar for the language of well-formed, executable commands, and (3) a dynamic Backend Model (BEM) that maintains updated status for salient aspects of the non-linguistic context. In this paper we describe its four-step recovery algorithm invoked by DM whenever an item is unclear in the current context, or when an interpretation error is, and show how parameter settings on the algorithm can modify the overall behavior of the system from Tutor to Trainer. This is offered to illustrate how limited (inexpensive) dialogue processing functionality, judiciously selected, and designed in c...

The three-tiered discourse representation defined in (Luperfoy, 1991) is applied to multimodal humancomputer interface (HCI) dialogues. In the applied system the three fiers are (1) a linguistic analysis (morphological, syntactic, sentential semantic) of input and output communicative events including keyboard-entered command language atoms, NL strings, mouse clicks, output text strings, and output graphical events; (2) a discourse model representation containing one discourse object, called a peg, for each construct (each guise of an individual) under discussion; and (3) the knowledge base (KB) representation of the computer agent's 'belief' system which is used to support its interpretation procedures. I present evidence to justify the added complexity of this three-tiered system over standard two-tiered representations, based on (A) cognitive processes that must be supported for any non-idealized dialogue environment (e.g., the agents can discuss constructs not present in their current belief systems), including information decay, and the need for a distinction between understanding a discourse and believing the information content of a discourse; (B) linguistic phenomena, in particular, context-dependent NPs, which can be partially or totally anaphoric; and (C) observed requirements of three implemented HCI dialogue systems that have employed this three-tiered discourse representation.