659 research outputs found
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
Description Theory, LTAGs and Underspecified Semantics
An attractive way to model
the relation between an underspecified syntactic representation and
its completions is to let the underspecified representation correspond
to a logical description and the completions to the
models of that description. This approach, which underlies the
Description Theory of (Marcus et al. 1983) has been integrated
in (Vijay-Shanker 1992) with a pure unification approach to
Lexicalized Tree-Adjoining
Grammars (Joshi et al.\ 1975, Schabes 1990). We generalize
Description Theory by integrating semantic
information, that is, we propose to tackle both syntactic and
semantic underspecification using descriptions
MOG 2007:Workshop on Multimodal Output Generation: CTIT Proceedings
This volume brings together presents a wide variety of work offering different perspectives on multimodal generation. Two different strands of work can be distinguished: half of the gathered papers present current work on embodied conversational agents (ECA’s), while the other half presents current work on multimedia applications. Two general research questions are shared by all: what output modalities are most suitable in which situation, and how should different output modalities be combined
Problem spotting in human-machine interaction
In human-human communication, dialogue participants are con-tinuously sending and receiving signals on the status of the inform-ation being exchanged. We claim that if spoken dialogue systems were able to detect such cues and change their strategy accordingly, the interaction between user and systemwould improve. Therefore, the goals of the present study are as follows: (i) to find out which positive and negative cues people actually use in human-machine interaction in response to explicit and implicit verification questions and (ii) to see which (combinations of) cues have the best predictive potential for spotting the presence or absence of problems. It was found that subjects systematically use negative/marked cues (more words, marked word order, more repetitions and corrections, less new information etc.) when there are communication problems. Using precision and recall matrices it was found that various combinations of cues are accurate problem spotters. This kind of information may turn out to be highly relevant for spoken dia-logue systems, e.g., by providing quantitative criteria for changing the dialogue strategy or speech recognition engine
Does Size Matter – How Much Data is Required to Train a REG Algorithm?
In this paper we investigate how much data is required to train an algorithm for attribute selection, a subtask of Referring Expressions Generation (REG). To enable comparison between different-sized training sets, a systematic training method was developed. The results show that depending on the complexity of the domain, training on 10 to 20 items may already lead to a good performance
Effects of domain size during reference production in photo-realistic scenes
The current study investigates how speakers are affected by the size of the visual domain during reference production. Previous research found that speech onset times increase along with the number of distractors that are visible, at least when speakers refer to non-salient target objects in simplified visual domains. This suggests that in the case of more distractors, speakers need more time to perform an object-by-object scan of all distractors that are visible. We present the results of a reference production experiment, to study if this pattern for speech onset times holds for photo-realistic scenes, and to test if the suggested viewing strategy is reflected directly in speakers’ eye movements. Our results show that this is indeed the case: we find (1) that speech onset times increase linearly as more distractors are present; (2) that speakers fixate the target relatively less often in larger domains; and (3) that larger domains elicit more fixation switches back and forth between the target and its distractors
Realizing the Costs: Template-Based Surface Realisation in the GRAPH Approach to Referring Expression Generation
We describe a new realiser developed for the TUNA 2009 Challenge, and present its evaluation scores on the development set, showing a clear increase in performance compared to last year’s simple realiser
Annotating a Parallel Monolingual Treebank with Semantic Similarity Relations
Proceedings of the Sixth International Workshop on Treebanks and
Linguistic Theories.
Editors: Koenraad De Smedt, Jan Hajič and Sandra Kübler.
NEALT Proceedings Series, Vol. 1 (2007), 85-96.
© 2007 The editors and contributors.
Published by
Northern European Association for Language
Technology (NEALT)
http://omilia.uio.no/nealt .
Electronically published at
Tartu University Library (Estonia)
http://hdl.handle.net/10062/4476
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