625 research outputs found
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
The Riemannian product structures of spacelike hypersurfaces with constant k-th mean curvature in the de Sitter spaces
AbstractIn this paper, we investigate complete spacelike hypersurfaces in the de Sitter space S1n+1(c) with constant k-th mean curvature and two distinct principal curvatures one of which is simple. We obtain some characterizations of the Riemannian product H1(c1)×Sn−1(c2) or Hn−1(c1)×S1(c2) in the de Sitter space S1n+1(c)
Micro Finite Element Simulation of Cutting Process of SiCp/Al Particle Reinforced Composites
In order to reveal the cutting mechanism of SiCp/Al particle reinforced composites, the micro structure model of random distribution of particles was established based on Python script language by using ABAQUS, the prediction of machined surface morphology and chip morphology was realized. The validity of the model is verified by comparing with the experimental results
Revision in Continuous Space: Unsupervised Text Style Transfer without Adversarial Learning
Typical methods for unsupervised text style transfer often rely on two key
ingredients: 1) seeking the explicit disentanglement of the content and the
attributes, and 2) troublesome adversarial learning. In this paper, we show
that neither of these components is indispensable. We propose a new framework
that utilizes the gradients to revise the sentence in a continuous space during
inference to achieve text style transfer. Our method consists of three key
components: a variational auto-encoder (VAE), some attribute predictors (one
for each attribute), and a content predictor. The VAE and the two types of
predictors enable us to perform gradient-based optimization in the continuous
space, which is mapped from sentences in a discrete space, to find the
representation of a target sentence with the desired attributes and preserved
content. Moreover, the proposed method naturally has the ability to
simultaneously manipulate multiple fine-grained attributes, such as sentence
length and the presence of specific words, when performing text style transfer
tasks. Compared with previous adversarial learning based methods, the proposed
method is more interpretable, controllable and easier to train. Extensive
experimental studies on three popular text style transfer tasks show that the
proposed method significantly outperforms five state-of-the-art methods.Comment: Association for the Advancement of Artificial Intelligence. AAAI 202
Volumetric Tooth Wear Measurement of Scraper Conveyor Sprocket Using Shape from Focus-Based Method
Volumetric tooth wear measurement is important to assess the life of scraper conveyor sprocket. A shape from focus-based method is used to measure scraper conveyor sprocket tooth wear. This method reduces the complexity of the process and improves the accuracy and efficiency of existing methods. A prototype set of sequence images taken by the camera facing the sprocket teeth is collected by controlling the fabricated track movement. In this method, a normal distribution operator image filtering is employed to improve the accuracy of an evaluation function value calculation. In order to detect noisy pixels, a normal operator is used, which involves with using a median filter to retain as much of the original image information as possible. In addition, an adaptive evaluation window selection method is proposed to address the difficulty associated with identifying an appropriate evaluation window to calculate the focused evaluation value. The shape and size of the evaluation window are autonomously determined using the correlation value of the grey scale co-occurrence matrix generated from the measured pixels’ neighbourhood pixels. A reverse engineering technique is used to quantitatively verify the shape volume recovery accuracy of different evaluation windows. The test results demonstrate that the proposed method can effectively measure sprocket teeth wear volume with an accuracy up to 97.23
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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