A model of revision in natural language generation

Vaughan, Marie M.; McDonald, David D.

doi:10.3115/981131.981146

Cited by 19 publications

(8 citation statements)

References 5 publications

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“…In similar work, Vaughan (1986) has proposed to use a plan and critique cycle to produce text. For modeling extemporaneous language production, however, critiquing is too time~consuming, and more reliable planning mechanisms are needed.…”

Section: Related Workmentioning

confidence: 99%

Modeling extemporaneous elaboration

Bienkowski¹

1987

Proceedings of the Third Conference on European Chapter of the Association for Computational Linguistics -

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Intelligent problem solving systems must be able to express their results in a coherent and flexible manner. One way this can be done is by eztemporaneous elaboration, the method of language production that underlies more skilled tasks such as explanation. This paper outlines a computational model for extemporaneous elaboration that is implemented in a computer model called Extemper, shows examples of its operation, and compares it with other models of language production. Extemper contains the four components minimally required for elaboration: 1) an efficient method for linearizing a knowledge structure, 2) a translation/selection mechanism for producing a conceptual textbase from the knowledge structure, 3) local coherence operators which provide local connections between textbase elements, and 4) a conceptual generator to translate the coherent textbase into English.

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Section: Related Workmentioning

confidence: 99%

Modeling extemporaneous elaboration

Bienkowski¹

1987

Proceedings of the Third Conference on European Chapter of the Association for Computational Linguistics -

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“…The model of a revision was first brought into discussion by Vaughan and McDonald [79], but more practical definition can be found in [45]. Later the concept was successfully implemented in NLG systems in early developments [69,22].…”

Section: Revision Architecturesmentioning

confidence: 99%

Recent Advances in Natural Language Generation: A Survey and Classification of the Empirical Literature

Perera

Nand

2017

cai

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Abstract. Natural Language Generation (NLG) is defined as the systematic approach for producing human understandable natural language text based on nontextual data or from meaning representations. This is a significant area which empowers human-computer interaction. It has also given rise to a variety of theoretical as well as empirical approaches. This paper intends to provide a detailed overview and a classification of the state-of-the-art approaches in Natural Language Generation. The paper explores NLG architectures and tasks classed under document planning, micro-planning and surface realization modules. Additionally, this paper also identifies the gaps existing in the NLG research which require further work in order to make NLG a widely usable technology.

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“…Basically, this requires NLG techniques which do not take non-linguistic information as input, but rather (possibly ungrammatical) linguistic information (phrases or text fragments), and as a result this approach to NLG is sometimes referred to as text-to-text generation. It bears a strong conceptual resemblance to text revision, an area of NLG which received some scholarly attention in the 1980s and 1990s (e.g., [8,9]). It has turned out that text-to-text generation lends itself well for data-oriented approaches, in part because textual training and evaluation material are easy to come by.…”

Section: Prefacementioning

confidence: 99%