11 research outputs found
Integrating Discourse Markers into a Pipelined Natural Language Generation Architecture
Pipelined Natural Language Generation (NLG) systems have grown increasingly complex as architectural modules were added to support language functionalities such as referring expressions, lexical choice, and revision. This has given rise to discussions about the relative placement of these new modules in the overall architecture
Aggregation with Recombination Patterns
In this paper, we show the commonalities between aggregation processes in Natural Language Generation and recombination patterns, a framework introduced recently as a way of generating complex sentences in natural languages using very simple recombination –and therefore biological– rules. By showing similarities between these two mechanisms, we suggest the possibility of carrying out aggregation by means of recombination patterns. We also refer to the possibility of using such a biological-motivated framework in the design of efficient and simple natural language generation devices
Prosody Modelling in Concept-to-Speech Generation: Methodological Issues
We explore three issues for the development of concept-to-speech (CTS) systems. We identify information available in a language-generation system that has the potential to impact prosody; investigate the role played by different corpora in CTS prosody modelling; and explore different methodologies for learning how linguistic features
impact prosody. Our major focus is on the comparison of two machine learning methodologies: generalized rule induction and memory-based learning. We describe this work in the context of multimedia abstract generation of intensive care (MAGIC) data, a system that produces multimedia brings of the status of patients who have just undergone a bypass operation
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
Microplanning with Communicative Intentions: The SPUD System
The process of microplanning in Natural Language Generation (NLG) encompasses a range of problems in which a generator must bridge underlying domain-specific representations and general linguistic representations. These problems include constructing linguistic referring expressions to identify domain objects, selecting lexical items to express domain concepts, and using complex linguistic constructions to concisely convey related domain facts. In this paper, we argue that such problems are best solved through a uniform, comprehensive, declarative process. In our approach, the generator directly explores a search space for utterances described by a linguistic grammar. At each stage of search, the generator uses a model of interpretation, which characterizes the potential links between the utterance and the domain and context, to assess its progress in conveying domain-specific representations. We further address the challenges for implementation and knowledge representation in this approach. We show how to implement this approach effectively by using the lexicalized tree-adjoining grammar formalism (LTAG) to connect structure to meaning and using modal logic programming to connect meaning to context. We articulate a detailed methodology for designing grammatical and conceptua
Desarrollo y uso de un software de generación de noticias deportivas con sentimiento en la mejora de procesos en el ámbito periodístico
La generación de texto dejó de ser del dominio exclusivo de los humanos hace años.
Hoy en día, existen sistemas de generación de lenguaje natural que escriben resúmenes de
documentos; código para construir aplicaciones y textos de todo tipo, incluyendo noticias.
Además, cada vez un número mayor de organizaciones son conscientes de los avances que
se producen en este campo y adoptan tecnología relacionada para reducir el tiempo que
sus trabajadores emplean, recortar sus gastos, etc.
Aquí se presenta un trabajo multidisciplinar, en el entorno de una Cátedra en la que
colaboran la Universidad Carlos III de Madrid y la Corporación de Radio y Televisión
Española, S. A. En él se exponen el desarrollo de una herramienta de generación de noticias
deportivas capaz de redactar el texto en función de la afición a la que vaya dirigida
la noticia, y la guía para la integración de la mencionada herramienta en la corporación
siguiendo el Ciclo de Mejora de los Procesos de negocio.Text generation ceased to be exclusive human domain years ago. Currently, there
exists Natural Language Generation (NLG) system that synthesize abstracts, generate code
for building applications, and write other texts, including news. Additionally, more
and more organizations are becoming aware of the progress that is being made in the
field and are including NLG technology into their structures to reduce expenses and help
employees save time.
This document displays a multidisciplinary thesis within the fellowship involving Universidad
Carlos III de Madrid and Corporación de Radio y Televisión Española S. A. The
presented work depicts the development of an automatic sports news generator that can
tailor the text depending on the sports fans the text is intended to. Furthermore, a guide is
provided on how to incorporate such tool in RTVE following CMP, a continuous business
process improvement methodology.Doble Grado en Ingeniería Informática y Administración de Empresa
Semantic consistency in text generation
Automatic input-grounded text generation tasks process input texts and generate human-understandable natural language text for the processed information. The development
of neural sequence-to-sequence (seq2seq) models, which are usually trained in an end-to-end fashion, pushed the frontier of the performance on text generation tasks expeditiously. However, they are claimed to be defective in semantic consistency w.r.t. their
corresponding input texts. Also, not only the models are to blame. The corpora themselves always include examples whose output is semantically inconsistent to its input.
Any model that is agnostic to such data divergence issues will be prone to semantic inconsistency. Meanwhile, the most widely-used overlap-based evaluation metrics
comparing the generated texts to their corresponding references do not evaluate the
input-output semantic consistency explicitly, which makes this problem hard to detect.
In this thesis, we focus on studying semantic consistency in three automatic text
generation scenarios: Data-to-text Generation, Single Document Abstractive Summarization, and Chit-chat Dialogue Generation, by seeking for the answers to the following research questions: (1) how to define input-output semantic consistency in different
text generation tasks? (2) how to quantitatively evaluate the input-output semantic
consistency? (3) how to achieve better semantic consistency in individual tasks?
We systematically define the semantic inconsistency phenomena in these three
tasks as omission, intrinsic hallucination, and extrinsic hallucination. For Data-to-text Generation, we jointly learn a sentence planner that tightly controls which part
of input source gets generated in what sequence, with a neural seq2seq text generator,
to decrease all three types of semantic inconsistency in model-generated texts. The
evaluation results confirm that the texts generated by our model contain much less
omissions while maintaining low level of extrinsic hallucinations without sacrificing
fluency compared to seq2seq models. For Single Document Abstractive Summarization, we reduce the level of extrinsic hallucinations in training data by automatically
introducing assisting articles to each document-summary instance to provide the supplemental world-knowledge that is present in the summary but missing from the doc ument. With the help of a novel metric, we show that seq2seq models trained with as sisting articles demonstrate less extrinsic hallucinations than the ones trained without
them. For Chit-chat Dialogue Generation, by filtering out the omitted and hallucinated
examples from training set using a newly introduced evaluation metric, and encoding
it into the neural seq2seq response generation models as a control factor, we diminish
the level of omissions and extrinsic hallucinations in the generated dialogue responses