Microplanning with Communicative Intentions: The SPUD System

The process of microplanning in Natural Language Generation (NLG) encompasses a range of problems in which a generator must bridge underlying domain-specific representations and general linguistic representations. These problems include constructing linguistic referring expressions to identify domain objects, selecting lexical items to express domain concepts, and using complex linguistic constructions to concisely convey related domain facts. In this paper, we argue that such problems are best solved through a uniform, comprehensive, declarative process. In our approach, the generator directly explores a search space for utterances described by a linguistic grammar. At each stage of search, the generator uses a model of interpretation, which characterizes the potential links between the utterance and the domain and context, to assess its progress in conveying domain-specific representations. We further address the challenges for implementation and knowledge representation in this approach. We show how to implement this approach effectively by using the lexicalized tree-adjoining grammar formalism (LTAG) to connect structure to meaning and using modal logic programming to connect meaning to context. We articulate a detailed methodology for designing grammatical and conceptua

Corrections and Higher Order Unification

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An end-to-end evaluation of two situated dialog systems

We present and evaluate two state-of-the art dialogue systems developed to support dialog with French speaking virtual characters in the context of a serious game: one hybrid statistical/symbolic and one purely statistical. We conducted a quantitative evaluation where we compare the accuracy of the interpreter and of the dialog manager used by each system; a user based evaluation based on 22 subjects using both the statistical and the hybrid system; and a corpus based evaluation where we examine such criteria as dialog coherence, dialog success, interpretation and generation errors in the corpus of Human-System interactions collected during the user-based evaluation. We show that although the statistical approach is slightly more robust, the hybrid strategy seems to be better at guiding the player through the game