62,550 research outputs found
Learning Content Selection Rules for Generating Object Descriptions in Dialogue
A fundamental requirement of any task-oriented dialogue system is the ability
to generate object descriptions that refer to objects in the task domain. The
subproblem of content selection for object descriptions in task-oriented
dialogue has been the focus of much previous work and a large number of models
have been proposed. In this paper, we use the annotated COCONUT corpus of
task-oriented design dialogues to develop feature sets based on Dale and
Reiters (1995) incremental model, Brennan and Clarks (1996) conceptual pact
model, and Jordans (2000b) intentional influences model, and use these feature
sets in a machine learning experiment to automatically learn a model of content
selection for object descriptions. Since Dale and Reiters model requires a
representation of discourse structure, the corpus annotations are used to
derive a representation based on Grosz and Sidners (1986) theory of the
intentional structure of discourse, as well as two very simple representations
of discourse structure based purely on recency. We then apply the
rule-induction program RIPPER to train and test the content selection component
of an object description generator on a set of 393 object descriptions from the
corpus. To our knowledge, this is the first reported experiment of a trainable
content selection component for object description generation in dialogue.
Three separate content selection models that are based on the three theoretical
models, all independently achieve accuracies significantly above the majority
class baseline (17%) on unseen test data, with the intentional influences model
(42.4%) performing significantly better than either the incremental model
(30.4%) or the conceptual pact model (28.9%). But the best performing models
combine all the feature sets, achieving accuracies near 60%. Surprisingly, a
simple recency-based representation of discourse structure does as well as one
based on intentional structure. To our knowledge, this is also the first
empirical comparison of a representation of Grosz and Sidners model of
discourse structure with a simpler model for any generation task
Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation
This paper surveys the current state of the art in Natural Language
Generation (NLG), defined as the task of generating text or speech from
non-linguistic input. A survey of NLG is timely in view of the changes that the
field has undergone over the past decade or so, especially in relation to new
(usually data-driven) methods, as well as new applications of NLG technology.
This survey therefore aims to (a) give an up-to-date synthesis of research on
the core tasks in NLG and the architectures adopted in which such tasks are
organised; (b) highlight a number of relatively recent research topics that
have arisen partly as a result of growing synergies between NLG and other areas
of artificial intelligence; (c) draw attention to the challenges in NLG
evaluation, relating them to similar challenges faced in other areas of Natural
Language Processing, with an emphasis on different evaluation methods and the
relationships between them.Comment: Published in Journal of AI Research (JAIR), volume 61, pp 75-170. 118
pages, 8 figures, 1 tabl
Individual and Domain Adaptation in Sentence Planning for Dialogue
One of the biggest challenges in the development and deployment of spoken
dialogue systems is the design of the spoken language generation module. This
challenge arises from the need for the generator to adapt to many features of
the dialogue domain, user population, and dialogue context. A promising
approach is trainable generation, which uses general-purpose linguistic
knowledge that is automatically adapted to the features of interest, such as
the application domain, individual user, or user group. In this paper we
present and evaluate a trainable sentence planner for providing restaurant
information in the MATCH dialogue system. We show that trainable sentence
planning can produce complex information presentations whose quality is
comparable to the output of a template-based generator tuned to this domain. We
also show that our method easily supports adapting the sentence planner to
individuals, and that the individualized sentence planners generally perform
better than models trained and tested on a population of individuals. Previous
work has documented and utilized individual preferences for content selection,
but to our knowledge, these results provide the first demonstration of
individual preferences for sentence planning operations, affecting the content
order, discourse structure and sentence structure of system responses. Finally,
we evaluate the contribution of different feature sets, and show that, in our
application, n-gram features often do as well as features based on higher-level
linguistic representations
How Do I Address You? Modelling addressing behavior based on an analysis of a multi-modal corpora of conversational discourse
Addressing is a special kind of referring and thus principles of multi-modal referring expression generation will also be basic for generation of address terms and addressing gestures for conversational agents. Addressing is a special kind of referring because of the different (second person instead of object) role that the referent has in the interaction. Based on an analysis of addressing behaviour in multi-party face-to-face conversations (meetings, TV discussions as well as theater plays), we present outlines of a model for generating multi-modal verbal and non-verbal addressing behaviour for agents in multi-party interactions
- âŠ