3,671 research outputs found
How Images Inspire Poems: Generating Classical Chinese Poetry from Images with Memory Networks
With the recent advances of neural models and natural language processing,
automatic generation of classical Chinese poetry has drawn significant
attention due to its artistic and cultural value. Previous works mainly focus
on generating poetry given keywords or other text information, while visual
inspirations for poetry have been rarely explored. Generating poetry from
images is much more challenging than generating poetry from text, since images
contain very rich visual information which cannot be described completely using
several keywords, and a good poem should convey the image accurately. In this
paper, we propose a memory based neural model which exploits images to generate
poems. Specifically, an Encoder-Decoder model with a topic memory network is
proposed to generate classical Chinese poetry from images. To the best of our
knowledge, this is the first work attempting to generate classical Chinese
poetry from images with neural networks. A comprehensive experimental
investigation with both human evaluation and quantitative analysis demonstrates
that the proposed model can generate poems which convey images accurately.Comment: Accepted by AAAI 201
Adversarial Generation of Natural Language
Generative Adversarial Networks (GANs) have gathered a lot of attention from
the computer vision community, yielding impressive results for image
generation. Advances in the adversarial generation of natural language from
noise however are not commensurate with the progress made in generating images,
and still lag far behind likelihood based methods. In this paper, we take a
step towards generating natural language with a GAN objective alone. We
introduce a simple baseline that addresses the discrete output space problem
without relying on gradient estimators and show that it is able to achieve
state-of-the-art results on a Chinese poem generation dataset. We present
quantitative results on generating sentences from context-free and
probabilistic context-free grammars, and qualitative language modeling results.
A conditional version is also described that can generate sequences conditioned
on sentence characteristics.Comment: 11 pages, 3 figures, 5 table
Deep Poetry: A Chinese Classical Poetry Generation System
In this work, we demonstrate a Chinese classical poetry generation system
called Deep Poetry. Existing systems for Chinese classical poetry generation
are mostly template-based and very few of them can accept multi-modal input.
Unlike previous systems, Deep Poetry uses neural networks that are trained on
over 200 thousand poems and 3 million ancient Chinese prose. Our system can
accept plain text, images or artistic conceptions as inputs to generate Chinese
classical poetry. More importantly, users are allowed to participate in the
process of writing poetry by our system. For the user's convenience, we deploy
the system at the WeChat applet platform, users can use the system on the
mobile device whenever and wherever possible. The demo video of this paper is
available at https://youtu.be/jD1R_u9TA3M.Comment: Association for the Advancement of Artificial Intelligence,
Demonstrations Program. AAAI 202
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