6,509 research outputs found
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
Beyond Narrative Description: Generating Poetry from Images by Multi-Adversarial Training
Automatic generation of natural language from images has attracted extensive
attention. In this paper, we take one step further to investigate generation of
poetic language (with multiple lines) to an image for automatic poetry
creation. This task involves multiple challenges, including discovering poetic
clues from the image (e.g., hope from green), and generating poems to satisfy
both relevance to the image and poeticness in language level. To solve the
above challenges, we formulate the task of poem generation into two correlated
sub-tasks by multi-adversarial training via policy gradient, through which the
cross-modal relevance and poetic language style can be ensured. To extract
poetic clues from images, we propose to learn a deep coupled visual-poetic
embedding, in which the poetic representation from objects, sentiments and
scenes in an image can be jointly learned. Two discriminative networks are
further introduced to guide the poem generation, including a multi-modal
discriminator and a poem-style discriminator. To facilitate the research, we
have released two poem datasets by human annotators with two distinct
properties: 1) the first human annotated image-to-poem pair dataset (with 8,292
pairs in total), and 2) to-date the largest public English poem corpus dataset
(with 92,265 different poems in total). Extensive experiments are conducted
with 8K images, among which 1.5K image are randomly picked for evaluation. Both
objective and subjective evaluations show the superior performances against the
state-of-the-art methods for poem generation from images. Turing test carried
out with over 500 human subjects, among which 30 evaluators are poetry experts,
demonstrates the effectiveness of our approach
TIGS: An Inference Algorithm for Text Infilling with Gradient Search
Text infilling is defined as a task for filling in the missing part of a
sentence or paragraph, which is suitable for many real-world natural language
generation scenarios. However, given a well-trained sequential generative
model, generating missing symbols conditioned on the context is challenging for
existing greedy approximate inference algorithms. In this paper, we propose an
iterative inference algorithm based on gradient search, which is the first
inference algorithm that can be broadly applied to any neural sequence
generative models for text infilling tasks. We compare the proposed method with
strong baselines on three text infilling tasks with various mask ratios and
different mask strategies. The results show that our proposed method is
effective and efficient for fill-in-the-blank tasks, consistently outperforming
all baselines.Comment: The 57th Annual Meeting of the Association for Computational
Linguistics (ACL 2019
Constrained structure of ancient Chinese poetry facilitates speech content grouping
Ancient Chinese poetry is constituted by structured language that deviates from ordinary language usage [1, 2]; its poetic genres impose unique combinatory constraints on linguistic elements [3]. How does the constrained poetic structure facilitate speech segmentation when common linguistic [4, 5, 6, 7, 8] and statistical cues [5, 9] are unreliable to listeners in poems? We generated artificial Jueju, which arguably has the most constrained structure in ancient Chinese poetry, and presented each poem twice as an isochronous sequence of syllables to native Mandarin speakers while conducting magnetoencephalography (MEG) recording. We found that listeners deployed their prior knowledge of Jueju to build the line structure and to establish the conceptual flow of Jueju. Unprecedentedly, we found a phase precession phenomenon indicating predictive processes of speech segmentation—the neural phase advanced faster after listeners acquired knowledge of incoming speech. The statistical co-occurrence of monosyllabic words in Jueju negatively correlated with speech segmentation, which provides an alternative perspective on how statistical cues facilitate speech segmentation. Our findings suggest that constrained poetic structures serve as a temporal map for listeners to group speech contents and to predict incoming speech signals. Listeners can parse speech streams by using not only grammatical and statistical cues but also their prior knowledge of the form of language
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
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