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Sentence Generation as a Planning Problem
In this paper, we translate sentence generation from TAG grammars with semantic and pragmatic information into a planning problem by encoding the contribution of each word declaratively and explicitly. This allows us to tap into the recent performance improvements in off-the-shelf planners. It also opens up new perspectives on referring expression generation and the relationship between language and action
A Planning-based Approach for Music Composition
. Automatic music composition is a fascinating field within computational
creativity. While different Artificial Intelligence techniques have been used
for tackling this task, Planning – an approach for solving complex combinatorial
problems which can count on a large number of high-performance systems and
an expressive language for describing problems – has never been exploited.
In this paper, we propose two different techniques that rely on automated planning
for generating musical structures. The structures are then filled from the bottom
with “raw” musical materials, and turned into melodies. Music experts evaluated
the creative output of the system, acknowledging an overall human-enjoyable
trait of the melodies produced, which showed a solid hierarchical structure and a
strong musical directionality. The techniques proposed not only have high relevance
for the musical domain, but also suggest unexplored ways of using planning
for dealing with non-deterministic creative domains
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
Event Representations for Automated Story Generation with Deep Neural Nets
Automated story generation is the problem of automatically selecting a
sequence of events, actions, or words that can be told as a story. We seek to
develop a system that can generate stories by learning everything it needs to
know from textual story corpora. To date, recurrent neural networks that learn
language models at character, word, or sentence levels have had little success
generating coherent stories. We explore the question of event representations
that provide a mid-level of abstraction between words and sentences in order to
retain the semantic information of the original data while minimizing event
sparsity. We present a technique for preprocessing textual story data into
event sequences. We then present a technique for automated story generation
whereby we decompose the problem into the generation of successive events
(event2event) and the generation of natural language sentences from events
(event2sentence). We give empirical results comparing different event
representations and their effects on event successor generation and the
translation of events to natural language.Comment: Submitted to AAAI'1
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
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