37,308 research outputs found
Generative Neural Machine Translation
We introduce Generative Neural Machine Translation (GNMT), a latent variable
architecture which is designed to model the semantics of the source and target
sentences. We modify an encoder-decoder translation model by adding a latent
variable as a language agnostic representation which is encouraged to learn the
meaning of the sentence. GNMT achieves competitive BLEU scores on pure
translation tasks, and is superior when there are missing words in the source
sentence. We augment the model to facilitate multilingual translation and
semi-supervised learning without adding parameters. This framework
significantly reduces overfitting when there is limited paired data available,
and is effective for translating between pairs of languages not seen during
training
Generative Neural Machine Translation
We introduce Generative Neural Machine Translation (GNMT), a latent variable architecture which is designed to model the semantics of the source and target sentences. We modify an encoder-decoder translation model by adding a latent variable as a language agnostic representation which is encouraged to learn the meaning of the sentence. GNMT achieves competitive BLEU scores on pure translation tasks, and is superior when there are missing words in the source sentence. We augment the model to facilitate multilingual translation and semi-supervised learning without adding parameters. This framework significantly reduces overfitting when there is limited paired data available, and is effective for translating between pairs of languages not seen during training
Auto-Encoding Variational Neural Machine Translation
We present a deep generative model of bilingual sentence pairs for machine
translation. The model generates source and target sentences jointly from a
shared latent representation and is parameterised by neural networks. We
perform efficient training using amortised variational inference and
reparameterised gradients. Additionally, we discuss the statistical
implications of joint modelling and propose an efficient approximation to
maximum a posteriori decoding for fast test-time predictions. We demonstrate
the effectiveness of our model in three machine translation scenarios:
in-domain training, mixed-domain training, and learning from a mix of
gold-standard and synthetic data. Our experiments show consistently that our
joint formulation outperforms conditional modelling (i.e. standard neural
machine translation) in all such scenarios
A Neural Network Approach to Context-Sensitive Generation of Conversational Responses
We present a novel response generation system that can be trained end to end
on large quantities of unstructured Twitter conversations. A neural network
architecture is used to address sparsity issues that arise when integrating
contextual information into classic statistical models, allowing the system to
take into account previous dialog utterances. Our dynamic-context generative
models show consistent gains over both context-sensitive and
non-context-sensitive Machine Translation and Information Retrieval baselines.Comment: A. Sordoni, M. Galley, M. Auli, C. Brockett, Y. Ji, M. Mitchell,
J.-Y. Nie, J. Gao, B. Dolan. 2015. A Neural Network Approach to
Context-Sensitive Generation of Conversational Responses. In Proc. of
NAACL-HLT. Pages 196-20
Latent Variable Model for Multi-modal Translation
In this work, we propose to model the interaction between visual and textual
features for multi-modal neural machine translation (MMT) through a latent
variable model. This latent variable can be seen as a multi-modal stochastic
embedding of an image and its description in a foreign language. It is used in
a target-language decoder and also to predict image features. Importantly, our
model formulation utilises visual and textual inputs during training but does
not require that images be available at test time. We show that our latent
variable MMT formulation improves considerably over strong baselines, including
a multi-task learning approach (Elliott and K\'ad\'ar, 2017) and a conditional
variational auto-encoder approach (Toyama et al., 2016). Finally, we show
improvements due to (i) predicting image features in addition to only
conditioning on them, (ii) imposing a constraint on the minimum amount of
information encoded in the latent variable, and (iii) by training on additional
target-language image descriptions (i.e. synthetic data).Comment: Paper accepted at ACL 2019. Contains 8 pages (11 including
references, 13 including appendix), 6 figure
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