The process of microplanning encompasses a range of problems in Natural Language Generation (NLG), such as referring expression generation, lexical choice, and aggregation, problems in which a generator must bridge underlying domain-specific representations and general linguistic representations. In this paper, we describe a uniform approach to microplanning based on declarative representations of a generator's communicative intent. These representations describe the RE- SULTS of NLG: communicative intent associates the concrete linguistic structure planned by the generator with inferences that show how the meaning of that structure communicates needed information about some application domain in the current discourse context. Our approach, implemented in the SPUD (sentence planning using description) microplanner, uses the lexicalized treeadjoining grammar formalism (LTAG) to connect structure to meaning and uses modal logic programming to connect meaning to context. At the same time, communicative intent representations provide a RESOURCE for the PROCESS of NLG. Using representations of communicative intent, a generator can augment the syntax, semantics and pragmatics of an incomplete sentence simultaneously, and can assess its progress on the various problems of microplanning incrementally. The declarative formulation of communicative intent translates into a well-defined methodology for designing grammatical and conceptual resources which the generator can use to achieve desired microplanning behavior in a specified domain. Contents 1 Motivation 3 2

In this paper we will present our ongoing work on a plan-based discourse processor developed in the context of the Enthusiast Spanish to English translation system as part of the JANUS multi-lingual speech-tospeech translation system. We will demonstrate that theories of discourse which pos- tulate a strict tree structure of discourse on either the intentional or attentional level are not totally adequate for handling spontaneous dialogues. We will present our extension to this approach along with its implementation in our plan-based dis- course processor. We will demonstrate that the implementation of our approach outperforms an implementation based on the strict tree structure approach.

The fields of user modeling and natural language processing have been closely linked since the early days of user modeling. Natural language systems consult user models in order to improve their understanding of users’ requirements and to generate appropriate and relevant responses. At the same time, the information natural language systems obtain from their users is expected to increase the accuracy of their user models. In this paper, we review natural language systems for generation, understanding and dialogue, focusing on the requirements and limitations these systems and user models place on each other. We then propose avenues for future research.

This chapter investigates the computational consequences of a broadly Gricean view of language use as intentional activity. In this view, dialogue rests on coordinated reasoning about communicative intentions. The speaker produces each utterance by formulating a suitable communicative intention. The hearer understands it by recognizing the communicative intention behind it. When this coordination is successful, interlocutors succeed in considering the same intentions— that is, the same representations of utterance meaning—as the dialogue proceeds. In this paper, I emphasize that these intentions can be formalized; we can provide abstract but systematic representations that spell out what a speaker is trying to do with an utterance. Such representations describe utterances simultaneously as the product of our knowledge of grammar and as actions chosen for a reason. In particular, they must characterize the speaker’s utterance in grammatical terms, provide the links to the context that the grammar requires, and so arrive at a contribution that the speaker aims to achieve. Because I have implemented this formalism, we can regard it as a possible analysis of conversational processes at the level of computational theory. Nevertheless, this analysis leaves open what the nature of the biological computation involved in inference to intentions is, and what regularities in language use support this computation.

A multi-agent based architecture for the Portuguese Attorney General's Oce documents is presented.

We describe a mechanism which generates rebuttals to a user's rejoinders in the context of arguments generated from Bayesian networks. This mechanism is implemented in an interactive argumentation system. Given an argument generated by the system and an interpretation of a user's rejoinder, the generation of the rebuttal takes into account the intended effect of the user's rejoinder, determined on a model of the user's beliefs, and its actual effect, determined on a model of the system's beliefs. We consider three main rebuttal strategies: refute the user's rejoinder, strengthen the argument goal, and dismiss the user's line of reasoning.

Abstract. We describe a mechanism which recognizes a user’s intentions from short-form rejoinders to arguments generated from Bayesian networks. The mechanism builds candidate reasoning paths that link the user’s rejoinder with a previously presented argument, and considers the following factors to select a path: linguistic clues, the impact of the user’s rejoinder on the system’s argument along the different paths, the user’s attentional focus, and the system’s confidence in its representation of the user’s beliefs. The results of our preliminary evaluation indicate that the interpretations produced by our mechanism are generally appropriate. 1

Abstract. We describe a probabilistic approach for the interpretation of user arguments, and investigate the incorporation of different models of a user’s beliefs and inferences into this mechanism. Our approach is based on the tenet that the interpretation intended by the user is that with the highest posterior probability. This approach is implemented in a computer-based detective game, where the user explores a virtual scenario, and constructs an argument for a suspect’s guilt or innocence. Our system receives as input an argument entered through a web interface, and produces an interpretation in terms of its underlying knowledge representation – a Bayesian network. This interpretation may differ from the user’s argument in its structure and in its beliefs in the argument propositions. We conducted a synthetic evaluation of the basic interpretation mechanism, and a user-based evaluation which assesses the impact of the different user models. The results of both evaluations were encouraging, with the system generally producing argument interpretations our users found acceptable. Key words. Bayesian networks, discourse interpretation, probabilistic approach 1.

In this paper, we claim that the disambiguation of referring expressions in discourse can be formulated in terms that automated reasoners can address. Specifically, we show that consistency, informativity and minimality are criteria which can be (i) implemented using automated reasoning tools and (ii) used to disambiguate noun-noun compounds, metonymy and definite descriptions.

This paper discusses the general plan inference paradigm, alternative approaches to plan recognition, techniques developed to address issues 1