341 research outputs found
Generating multimedia presentations: from plain text to screenplay
In many Natural Language Generation (NLG) applications, the output is limited to plain text – i.e., a string of words with punctuation and paragraph breaks, but no indications for layout, or pictures, or dialogue. In several projects, we have begun to explore NLG applications in which these extra media are brought into play. This paper gives an informal account of what we have learned. For coherence, we focus on the domain of patient information leaflets, and follow an example in which the same content is expressed first in plain text, then in formatted text, then in text with pictures, and finally in a dialogue script that can be performed by two animated agents. We show how the same meaning can be mapped to realisation patterns in different media, and how the expanded options for expressing meaning are related to the perceived style and tone of the presentation. Throughout, we stress that the extra media are not simple added to plain text, but integrated with it: thus the use of formatting, or pictures, or dialogue, may require radical rewording of the text itself
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
Formemes in English-Czech Deep Syntactic MT
One of the most notable recent improvements of the TectoMT English-to-Czech translation is a systematic and theoretically supported revision of formemes—the annotation of morpho-syntactic features of content words in deep dependency syntactic structures based on the Prague tectogrammatics theory. Our modifications aim at reducing data sparsity, increasing consistency across languages and widening the usage area of this markup. Formemes can be used not only in MT, but in various other NLP tasks
SESCORE2: Learning Text Generation Evaluation via Synthesizing Realistic Mistakes
Is it possible to train a general metric for evaluating text generation
quality without human annotated ratings? Existing learned metrics either
perform unsatisfactorily across text generation tasks or require human ratings
for training on specific tasks. In this paper, we propose SESCORE2, a
self-supervised approach for training a model-based metric for text generation
evaluation. The key concept is to synthesize realistic model mistakes by
perturbing sentences retrieved from a corpus. The primary advantage of the
SESCORE2 is its ease of extension to many other languages while providing
reliable severity estimation. We evaluate SESCORE2 and previous methods on four
text generation tasks across three languages. SESCORE2 outperforms unsupervised
metric PRISM on four text generation evaluation benchmarks, with a Kendall
improvement of 0.078. Surprisingly, SESCORE2 even outperforms the supervised
BLEURT and COMET on multiple text generation tasks. The code and data are
available at https://github.com/xu1998hz/SEScore2.Comment: Accepted at ACL2023 Main Conferenc
On the Development of Adaptive and User-Centred Interactive Multimodal Interfaces
Multimodal systems have attained increased attention in recent years, which has made possible important
improvements in the technologies for recognition, processing, and generation of multimodal information.
However, there are still many issues related to multimodality which are not clear, for example, the
principles that make it possible to resemble human-human multimodal communication. This chapter
focuses on some of the most important challenges that researchers have recently envisioned for future
multimodal interfaces. It also describes current efforts to develop intelligent, adaptive, proactive, portable
and affective multimodal interfaces
Fully generated scripted dialogue for embodied agents
This paper presents the NECA approach to the generation of dialogues between Embodied Conversational Agents (ECAs). This approach consist of the automated construction of an abstract script for an entire dialogue (cast in terms of dialogue acts), which is incrementally enhanced by a series of modules and finally ''performed'' by means of text, speech and body language, by a cast of ECAs. The approach makes it possible to automatically produce a large variety of highly expressive dialogues, some of whose essential properties are under the control of a user. The paper discusses the advantages and disadvantages of NECA's approach to Fully Generated Scripted Dialogue (FGSD), and explains the main techniques used in the two demonstrators that were built. The paper can be read as a survey of issues and techniques in the construction of ECAs, focusing on the generation of behaviour (i.e., focusing on information presentation) rather than on interpretation
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