5,195 research outputs found
A Stochastic Decoder for Neural Machine Translation
The process of translation is ambiguous, in that there are typically many
valid trans- lations for a given sentence. This gives rise to significant
variation in parallel cor- pora, however, most current models of machine
translation do not account for this variation, instead treating the prob- lem
as a deterministic process. To this end, we present a deep generative model of
machine translation which incorporates a chain of latent variables, in order to
ac- count for local lexical and syntactic varia- tion in parallel corpora. We
provide an in- depth analysis of the pitfalls encountered in variational
inference for training deep generative models. Experiments on sev- eral
different language pairs demonstrate that the model consistently improves over
strong baselines.Comment: Accepted at ACL 201
CanvasGAN: A simple baseline for text to image generation by incrementally patching a canvas
We propose a new recurrent generative model for generating images from text
captions while attending on specific parts of text captions. Our model creates
images by incrementally adding patches on a "canvas" while attending on words
from text caption at each timestep. Finally, the canvas is passed through an
upscaling network to generate images. We also introduce a new method for
generating visual-semantic sentence embeddings based on self-attention over
text. We compare our model's generated images with those generated Reed et.
al.'s model and show that our model is a stronger baseline for text to image
generation tasks.Comment: CVC 201
SALSA-TEXT : self attentive latent space based adversarial text generation
Inspired by the success of self attention mechanism and Transformer
architecture in sequence transduction and image generation applications, we
propose novel self attention-based architectures to improve the performance of
adversarial latent code- based schemes in text generation. Adversarial latent
code-based text generation has recently gained a lot of attention due to their
promising results. In this paper, we take a step to fortify the architectures
used in these setups, specifically AAE and ARAE. We benchmark two latent
code-based methods (AAE and ARAE) designed based on adversarial setups. In our
experiments, the Google sentence compression dataset is utilized to compare our
method with these methods using various objective and subjective measures. The
experiments demonstrate the proposed (self) attention-based models outperform
the state-of-the-art in adversarial code-based text generation.Comment: 10 pages, 3 figures, under review at ICLR 201
- …