5 research outputs found
NeuralREG: An end-to-end approach to referring expression generation
Traditionally, Referring Expression Generation (REG) models first decide on
the form and then on the content of references to discourse entities in text,
typically relying on features such as salience and grammatical function. In
this paper, we present a new approach (NeuralREG), relying on deep neural
networks, which makes decisions about form and content in one go without
explicit feature extraction. Using a delexicalized version of the WebNLG
corpus, we show that the neural model substantially improves over two strong
baselines. Data and models are publicly available.Comment: Accepted for presentation at ACL 201
Making effective use of healthcare data using data-to-text technology
Healthcare organizations are in a continuous effort to improve health
outcomes, reduce costs and enhance patient experience of care. Data is
essential to measure and help achieving these improvements in healthcare
delivery. Consequently, a data influx from various clinical, financial and
operational sources is now overtaking healthcare organizations and their
patients. The effective use of this data, however, is a major challenge.
Clearly, text is an important medium to make data accessible. Financial reports
are produced to assess healthcare organizations on some key performance
indicators to steer their healthcare delivery. Similarly, at a clinical level,
data on patient status is conveyed by means of textual descriptions to
facilitate patient review, shift handover and care transitions. Likewise,
patients are informed about data on their health status and treatments via
text, in the form of reports or via ehealth platforms by their doctors.
Unfortunately, such text is the outcome of a highly labour-intensive process if
it is done by healthcare professionals. It is also prone to incompleteness,
subjectivity and hard to scale up to different domains, wider audiences and
varying communication purposes. Data-to-text is a recent breakthrough
technology in artificial intelligence which automatically generates natural
language in the form of text or speech from data. This chapter provides a
survey of data-to-text technology, with a focus on how it can be deployed in a
healthcare setting. It will (1) give an up-to-date synthesis of data-to-text
approaches, (2) give a categorized overview of use cases in healthcare, (3)
seek to make a strong case for evaluating and implementing data-to-text in a
healthcare setting, and (4) highlight recent research challenges.Comment: 27 pages, 2 figures, book chapte
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