6,759 research outputs found
MixPoet: Diverse Poetry Generation via Learning Controllable Mixed Latent Space
As an essential step towards computer creativity, automatic poetry generation
has gained increasing attention these years. Though recent neural models make
prominent progress in some criteria of poetry quality, generated poems still
suffer from the problem of poor diversity. Related literature researches show
that different factors, such as life experience, historical background, etc.,
would influence composition styles of poets, which considerably contributes to
the high diversity of human-authored poetry. Inspired by this, we propose
MixPoet, a novel model that absorbs multiple factors to create various styles
and promote diversity. Based on a semi-supervised variational autoencoder, our
model disentangles the latent space into some subspaces, with each conditioned
on one influence factor by adversarial training. In this way, the model learns
a controllable latent variable to capture and mix generalized factor-related
properties. Different factor mixtures lead to diverse styles and hence further
differentiate generated poems from each other. Experiment results on Chinese
poetry demonstrate that MixPoet improves both diversity and quality against
three state-of-the-art models.Comment: 8 pages, 5 figures, published in AAAI 202
DeepCreativity: measuring creativity with deep learning techniques
Measuring machine creativity is one of the most fascinating challenges in Artificial Intelligence. This paper explores the possibility of using generative learning techniques for automatic assessment of creativity. The proposed solution does not involve human judgement, it is modular and of general applicability. We introduce a new measure, namely DeepCreativity, based on Margaret Boden’s definition of creativity as composed by value, novelty and surprise. We evaluate our methodology (and related measure) considering a case study, i.e., the generation of 19th century American poetry, showing its effectiveness and expressiveness
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
Creativity and Machine Learning: a Survey
There is a growing interest in the area of machine learning and creativity.
This survey presents an overview of the history and the state of the art of
computational creativity theories, machine learning techniques, including
generative deep learning, and corresponding automatic evaluation methods. After
presenting a critical discussion of the key contributions in this area, we
outline the current research challenges and emerging opportunities in this
field.Comment: 25 pages, 3 figures, 2 table
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