Probabilistic approaches for modeling text structure and their application to text-to-text generation

Since the early days of generation research, it has been acknowledged that modeling the global structure of a document is crucial for producing coherent, readable output. However, traditional knowledge-intensive approaches have been of limited utility in addressing this problem since they cannot be effectively scaled to operate in domain-independent, large-scale applications. Due to this difficulty, existing text-to-text generation systems rarely rely on such structural information when producing an output text. Consequently, texts generated by these methods do not match the quality of those written by humans – they are often fraught with severe coherence violations and disfluencies. In this chapter, I will present probabilistic models of document structure that can be effectively learned from raw document collections. This feature distinguishes these new models from traditional knowledge intensive approaches used in symbolic concept-to-text generation. Our results demonstrate that these probabilistic models can be directly applied to content organization, and suggest that these models can prove useful in an even broader range of text-to-text applications than we have considered here.National Science Foundation (U.S.) (CAREER grant IIS- 0448168)Microsoft Research. New Faculty Fellowshi

Task-Oriented Conversational Behavior of Agents for Collaboration in Human-Agent Teamwork

International audienceCoordination is an essential ingredient for human-agent teamwork. It requires team members to share knowledge to establish common grounding and mutual awareness among them. This paper proposes a be-havioral architecture C 2 BDI that enhances the knowledge sharing using natural language communication between team members. Collaborative conversation protocols and resource allocation mechanism have been defined that provide proactive behavior to agents for coordination. This architecture has been applied to a real scenario in a collaborative virtual environment for learning. The solution enables users to coordinate with other team members

Modeling the Dynamic Effects of Discourse: Principles and Frameworks

International audienceWhen studying the meaning of natural language expressions, sentence level provides a natural entry point. Its relevance of course depends on the focus we want to put on the meaning: as related to thought, to communication, to truth, etc. In this paper, we concentrate on the model theoretic view on meaning, in particular via first order logic representation. More specifically, we present phenomena that illustrate the challenges raised by discourse to truth-conditional semantics and compositionality. We show that proposals to address theses challenges rely on the additional device of contexts and on the way sentences can access and modify these contexts. This capability is usually referred to as the context change potential of a sentence. Depending on the phenomenon, the contexts need to represent different kinds of information: propositions, discourse referents, and variations on these elements. We also show that taking into account the rhetorical structure of discourse leads to even richer structuring of the context. Parallel to the presentation of the phenomena, we concentrate on formalisms giving an account of the dynamics of discourse. We introduce the well established formalisms of Discourse Representation Theory (DRT) Dynamic Predicate Logic (DPL) , and the more recently developed approach based on continuation semantics. Finally, we introduce Segmented Discourse Representation Theory (SDRT) that combines the effects of dynamics and discourse structure.La phrase représente un niveau d'entrée naturel dans l'étude du sens des expressions de la langue naturelle. Sa pertinence dépend bien sûr de la perspective que l'on souhaite mettre en avant sur le sens : celle relative à la pensée, celle relative à la communication, celle relative à la valeur de vérité... Dans cet article, nous nous concentrons sur la perspective donnée au sens par la théorie des modèles, en particulier par la représentation avec la logique du premier ordre. Plus spécifiquement, nous présentons des phénomènes illustrant les difficultés posées par le discours à la sémantique vériconditionnelle et à la compositionalité. Nous montrons que les propositions pour répondre à ces difficultés reposent sur l'utilisation de contextes et de la manière dons les phrases peuvent accéder à ce contexte et le modifier. Ce mécanisme est généralement appelé potentiel de changement du contexte d'une phrase. Suivant le phénomène considéré, le contexte doit représenter différents types d'informations : des propositions, des référents de discours, et des variations de ces éléments. Nous montrons également que la prise en compte de la structure rhétorique du discours conduit à une représentation encore plus riche du contexte. Parallèlement à l'exposé de ces phénomènes, nous présentons différents formalismes qui en rendent compte. Nous présentons la théorie des représentations discursives (DRT), de la logique des prédicats dynamique (DPL), ainsi que l'approche fondée sur les continuations proposée plus récemment. Enfin nous présentons la théorie des représentations discursives segmentées (SDRT) qui combine les effets de la dynamique et de la structure discursive