SlowGuess/ACL_2020_OCR huggingface dataset:
Yoon Kim, Sam Wiseman, Andrew Miller, David Sontag, and Alexander Rush. 2018. Semi-amortized variational autoencoders. In International Conference on Machine Learning, pages 2683-2692.
Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.
Diederik P Kingma, Tim Salimans, Rafal Jozefowicz, Xi Chen, Ilya Sutskever, and Max Welling. 2016. Improved variational inference with inverse autoregressive flow. In Advances in Neural Information Processing Systems, pages 4743-4751.
Diederik P. Kingma and Max Welling. 2014. Autoencoding variational bayes. In International Conference on Learning Representations.
Bohan Li, Junxian He, Graham Neubig, Taylor Berg-Kirkpatrick, and Yiming Yang. 2019. A surprisingly effective fix for deep latent variable modeling of text. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 3601-3612, Hong Kong, China. Association for Computational Linguistics.
Xiaodong Liu, Jianfeng Gao, Asli Celikyilmaz, Lawrence Carin, et al. 2019. Cyclic annealing schedule: A simple approach to mitigating KL vanishing. In NAACL.
Chunchuan Lyu and Ivan Titov. 2018. AMR parsing as graph prediction with latent alignment. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 397-407, Melbourne, Australia. Association for Computational Linguistics.
Xuezhe Ma, Chunting Zhou, Xian Li, Graham Neubig, and Eduard Hovy. 2019. FlowSeq: Nonautoregressive conditional sequence generation with generative flow. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 4281-4291, Hong Kong, China. Association for Computational Linguistics.
Mitchell P Marcus, Mary Ann Marcinkiewicz, and Beatrice Santorini. 1993. Building a large annotated corpus of english: The penn treebank. Computational linguistics, 19(2):313-330.
Gábor Melis, Chris Dyer, and Phil Blunsom. 2018. On the state of the art of evaluation in neural language models. In *ICLR*.
Yishu Miao and Phil Blunsom. 2016. Language as a latent variable: Discrete generative models for sentence compression. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pages 319-328. Association for Computational Linguistics.
Yishu Miao, Lei Yu, and Phil Blunsom. 2016. Neural variational inference for text processing. In International conference on machine learning, pages 1727-1736.
Tomáš Mikolov, Martin Karafiát, Lukáš Burget, Jan Černocký, and Sanjeev Khudanpur. 2010. Recurrent neural network based language model. In Eleventh Annual Conference of the International Speech Communication Association.
Yookoon Park, Jaemin Cho, and Gunhee Kim. 2018. A hierarchical latent structure for variational conversation modeling. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 1792-1801. Association for Computational Linguistics.
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer. 2017. Automatic differentiation in pytorch. In NIPS-W.
Yunchen Pu, Zhe Gan, Ricardo Henao, Xin Yuan, Chunyuan Li, Andrew Stevens, and Lawrence Carin. 2016. Variational autoencoder for deep learning of images, labels and captions. In D. D. Lee, M. Sugiyama, U. V. Luxburg, I. Guyon, and R. Garnett, editors, Advances in Neural Information Processing Systems 29, pages 2352-2360. Curran Associates, Inc.
Carl Edward Rasmussen and Christopher K. I. Williams. 2005. Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning). The MIT Press.
Ali Razavi, Aäron van den Oord, Ben Poole, and Oriol Vinyals. 2019. Preventing posterior collapse with delta-VAEs. In *ICLR*.
Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra. 2014. Stochastic backpropagation and approximate inference in deep generative models. In Proceedings of the 31st International Conference on Machine Learning, volume 32 of Proceedings of Machine Learning Research, pages 1278-1286, Beijing, China. PMLR.
Danilo Jimenez Rezende and Fabio Viola. 2018. Taming vaes. arXiv preprint arXiv:1810.00597.
Miguel Rios, Wilker Aziz, and Khalil Simaan. 2018. Deep generative model for joint alignment and word representation. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 1011-1023. Association for Computational Linguistics.
Herbert Robbins and Sutton Monro. 1951. A stochastic approximation method. The Annals of Mathematical Statistics, 22(3):400-407.
Philip Schulz, Wilker Aziz, and Trevor Cohn. 2018. A stochastic decoder for neural machine translation. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1243-1252. Association for Computational Linguistics.
Stanislau Semeniuta, Aliaksei Severyn, and Erhardt Barth. 2017. A hybrid convolutional variational autoencoder for text generation. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 627-637, Copenhagen, Denmark. Association for Computational Linguistics.
Iulian Vlad Serban, Alessandro Sordoni, Ryan Lowe, Laurent Charlin, Joelle Pineau, Aaron Courville, and Yoshua Bengio. 2017. A hierarchical latent variable encoder-decoder model for generating dialogues. In Thirty-First AAAI Conference on Artificial Intelligence.
Jasper Snoek, Hugo Larochelle, and Ryan P Adams. 2012. Practical bayesian optimization of machine learning algorithms. In Advances in neural information processing systems, pages 2951-2959.
Matthew Snover, Bonnie Dorr, Richard Schwartz, Linnea Micciulla, and John Makhoul. 2006. A study of translation edit rate with targeted human annotation. In Proceedings of association for machine translation in the Americas, volume 200.
Casper Kaae Sønderby, Tapani Raiko, Lars Maaløe, Søren Kaae Sønderby, and Ole Winther. 2016. Ladder variational autoencoders. In Advances in neural information processing systems, pages 3738-3746.
Ilya Tolstikhin, Olivier Bousquet, Sylvain Gelly, and Bernhard Schoelkopf. 2018. Wasserstein autoencoders. In ICLR.
Jakub M Tomczak and Max Welling. 2018. VAE with a VampPrior. In AISTATS.
Liwei Wang, Alexander Schwing, and Svetlana Lazebnik. 2017. Diverse and accurate image description using a variational auto-encoder with an additive gaussian encoding space. In I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, editors, Advances in Neural Information Processing Systems 30, pages 5756-5766. Curran Associates, Inc.
Tsung-Hsien Wen, Yishu Miao, Phil Blunsom, and Steve Young. 2017. Latent intention dialogue models. In Proceedings of the 34th International Conference on Machine Learning-Volume 70, pages 3732-3741. JMLR.org.
Jiacheng Xu and Greg Durrett. 2018. Spherical latent spaces for stable variational autoencoders. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 4503-4513, Brussels, Belgium. Association for Computational Linguistics.
Zichao Yang, Zhiting Hu, Ruslan Salakhutdinov, and Taylor Berg-Kirkpatrick. 2017. Improved variational autoencoders for text modeling using dilated convolutions. In Proceedings of the 34th International Conference on Machine Learning, volume 70 of Proceedings of Machine Learning Research, pages 3881-3890, International Convention Centre, Sydney, Australia. PMLR.
Wojciech Zaremba, Ilya Sutskever, and Oriol Vinyals. 2014. Recurrent neural network regularization. arXiv preprint arXiv:1409.2329.
Biao Zhang, Deyi Xiong, jinsong su, Hong Duan, and Min Zhang. 2016. Variational neural machine translation. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pages 521-530, Austin, Texas. Association for Computational Linguistics.
Shengjia Zhao, Jiaming Song, and Stefano Ermon. 2018a. The information autoencoding family: A lagrangian perspective on latent variable generative models. In Conference on Uncertainty in Artificial Intelligence, Monterey, California.
Shengjia Zhao, Jiaming Song, and Stefano Ermon. 2018b. InfoVAE: Information maximizing variational autoencoders. In Theoretical Foundations and Applications of Deep Generative Models, ICML18.
Tiancheng Zhao, Ran Zhao, and Maxine Eskenazi. 2017. Learning discourse-level diversity for neural dialog models using conditional variational autoencoders. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 654-664, Vancouver, Canada. Association for Computational Linguistics.
Chunting Zhou and Graham Neubig. 2017. Multi-space variational encoder-decoders for semisupervised labeled sequence transduction. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 310-320. Association for Computational Linguistics.
Zachary M Ziegler and Alexander M Rush. 2019. Latent normalizing flows for discrete sequences. arXiv preprint arXiv:1901.10548.
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A Architectures and Hyperparameters
In order to ensure that all our experiments are fully reproducible, this section provides an extensive overview of the model architectures, as well as model and optimisation hyperparameters.
Some hyperparameters are common to all experiments, e.g. optimiser and dropout, they can be found in Table 5. All models were optimised with Adam using default settings (Kingma and Ba, 2014). To regularise the models, we use dropout with a shared mask across time-steps (Zaremba et al., 2014) and weight decay proportional to the dropout rate (Gal and Ghahramani, 2015) on the input and output layers of the generative networks (i.e. RNNLM and the recurrent decoder in SENVAE). No dropout is applied to layers of the inference network as this does not lead to consistent empirical benefits and lacks a good theoretical basis. Gradient norms are clipped to prevent exploding gradients, and long sentences are truncated to three standard deviations above the average sentence length in the training data.
Parameter	Value
Optimizer	Adam
Optimizer Parameters	β1=0.9, β2=0.999
Learning Rate	0.001
Batch Size	64
DecoderDropout Rate(ρ)	0.4
Weight Decay	1-ρ/|D|
Maximum Sentence Length	59
Maximum Gradient Norm	1.5
This section describes the components that parameterise our models.17 We use mnemonic blocks layer(inputs; parameters) to describe architectures. Table 6 lists hyperparameters for the models discussed in what follows.
RNNLM At each step, an RNNLM parameterises a categorical distribution over the vocabulary, i.e.  $X_{i}|x_{<i} \sim \mathrm{Cat}(f(x_{<i};\theta))$ , where  $f(x_{<i};\theta) = \mathrm{softmax}(\mathbf{o}_i)$  and
$$
\mathbf {e} _ {i} = \operatorname {e m b} \left(x _ {i}; \theta_ {\text {e m b}}\right) \tag {12a}
$$
$$
\mathbf {h} _ {i} = \operatorname {G R U} \left(\mathbf {h} _ {i - 1}, \mathbf {e} _ {i - 1}; \theta_ {\mathrm {g r u}}\right) \tag {12b}
$$
$$
\mathbf {o} _ {i} = \operatorname {a f f i n e} \left(\mathbf {h} _ {i}; \theta_ {\text {o u t}}\right). \tag {12c}
$$
Table 5: Experimental settings.
Model	Parameter	Value
A	embedding units(de)	256
A	vocabulary size(dv)	25643
R and S	decoder layers(Lθ)	2
R and S	decoder hidden units(dh)	256
S	encoder hidden units(dλh)	256
S	encoder layers(Lλ)	1
S	latent units(dz)	32
MoG	mixture components(C)	100
VampPrior	pseudo inputs(C)	100
We employ an embedding layer (emb), one (or more) GRU cell(s)  $(\mathbf{h}_0 \in \theta)$  is a parameter of the model), and an affine layer to map from the dimensionality of the GRU to the vocabulary size. Table 7 compares our RNNLM to an external baseline with a comparable number of parameters.
Table 6: Architecture parameters: all (A), RNNLM (R), SENVAE (S).
Model	PPL↓	PPL Dyer ↓
Dyer et al. (2016)	93.5	113.4
RNNLM	84.5±0.52	102.1
Table 7: Baseline LMs on the PTB test set: avg ± std over 5 independent runs. Unlike us, Dyer et al. (2016) removed the end of sentence token for evaluation, thus the last column reports perplexity computed that way.
Gaussian SENVAE A Gaussian SENVAE also parameterises a categorical distribution over the vocabulary for each given prefix, but, in addition, it conditions on a latent embedding  $Z \sim \mathcal{N}(0,I)$ , i.e.  $X_{i}|z,x_{<i} \sim \operatorname{Cat}(f(z,x_{<i};\theta))$  where  $f(z,x_{<i};\theta) = \operatorname{softmax}(\mathbf{o}_i)$  and
$$
\mathbf {e} _ {i} = \operatorname {e m b} \left(x _ {i}; \theta_ {\text {e m b}}\right) \tag {13a}
$$
$$
\mathbf {h} _ {0} = \tanh  (\operatorname {a f f i n e} (z; \theta_ {\text {i n i t}})) \tag {13b}
$$
$$
\mathbf {h} _ {i} = \operatorname {G R U} \left(\mathbf {h} _ {i - 1}, \mathbf {e} _ {i - 1}; \theta_ {\mathrm {g r u}}\right) \tag {13c}
$$
$$
\mathbf {o} _ {i} = \operatorname {a f f i n e} \left(\mathbf {h} _ {i}; \theta_ {\text {o u t}}\right). \tag {13d}
$$
Compared to RNNLM, we modify  $f$  only slightly by initialising GRU cell(s) with  $\mathbf{h}_0$  computed as a learnt transformation of  $z$ . Because the marginal of the Gaussian SENVAE is intractable, we train it via variational inference using an inference model  $q(z|x,\lambda) = \mathcal{N}(z|\mathbf{u},\mathrm{diag}(\mathbf{s}\odot \mathbf{s}))$  where
$$
\mathbf {e} _ {i} = \operatorname {e m b} \left(x _ {i}; \theta_ {\text {e m b}}\right) \tag {14a}
$$
$$
\mathbf {h} _ {1} ^ {n} = \operatorname {B i G R U} \left(\mathbf {e} _ {1} ^ {n}, \mathbf {h} _ {0}; \lambda_ {\text {e n c}}\right) \tag {14b}
$$
$$
\mathbf {u} = \operatorname {a f f i n e} \left(\mathbf {h} _ {n}; \lambda_ {\text {l o c}}\right) \tag {14c}
$$
$$
\mathbf {s} = \text {s o f t p l u s} (\operatorname {a f f i n e} \left(\mathbf {h} _ {n}; \lambda_ {\text {s c a l e}}\right)). \tag {14d}
$$
17 All models were implemented with the PYTORCH library (Paszke et al., 2017), using default modules for the recurrent networks, embeddings and optimisers.
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Parameter	Value
Objective Function	Validation NLL
Kernel	Matern52
Acquisition Function	Expected Improvement
Parameter Inference	MCMC
MCMC Samples	10
Leapfrog Steps	20
Burn-in Samples	100
Table 8: Bayesian optimisation settings.
Note that we reuse the embedding layer from the generative model. Finally, a sample is obtained via  $z = \mathbf{u} + \mathbf{s}\odot \epsilon$  where  $\epsilon \sim \mathcal{N}(0,I_{dz})$ .
MoG prior We parameterise  $C$  diagonal Gaussians, which are mixed uniformly. To do so we need  $C$  location vectors, each in  $\mathbb{R}^{d_z}$ , and  $C$  scale vectors, each in  $\mathbb{R}_{>0}^{d_z}$ . To ensure strict positivity for scales we make  $\pmb{\sigma}^{(c)} = \mathrm{softplus}(\hat{\pmb{\sigma}}^{(c)})$ . The set of generative parameters  $\theta$  is therefore extended with  $\{\pmb{\mu}^{(c)}\}_{c=1}^C$  and  $\{\hat{\pmb{\sigma}}^{(c)}\}_{c=1}^C$ , each in  $\mathbb{R}^{d_z}$ .
VampPrior For this we estimate  $C$  sequences  $\{v^{(c)}\}_{c = 1}^{C}$  of input vectors, each sequence  $v^{(c)} = \langle \mathbf{v}_1^{(c)},\dots ,\mathbf{v}_{l_k}^{(c)}\rangle$  corresponds to a pseudo-input. This means we extend the set of generative parameters  $\theta$  with  $\{\mathbf{v}_i^{(c)}\}_{i = 1}^{l_c}$ , each in  $\mathbb{R}^{d_e}$ , for  $c = 1,\ldots ,C$ . For each  $c$ , we sample  $l_{c}$  at the beginning of training and keep it fixed. Specifically, we drew  $C$  samples from a normal,  $l_{c}\sim \mathcal{N}(\cdot |\mu_{l},\sigma_{l})$  which we rounded to the nearest integer.  $\mu_l$  and  $\sigma_l$  are the dataset sentence length mean and variance respectively.
A.2 Bayesian optimisation
Bayesian optimisation (BO) is an efficient method to approximately search for global optima of a (typically expensive to compute) objective function  $y = f(\mathbf{x})$ , where  $\mathbf{x} \in \mathbb{R}^{M}$  is a vector containing the values of  $M$  hyperparameters that may influence the outcome of the function (Snoek et al., 2012). Hence, it forms an alternative to grid search or random search (Bergstra and Bengio, 2012) for tuning the hyperparameters of a machine learning algorithm. BO works by assuming that our observations  $y_{n} | \mathbf{x}_{n}$  (for  $n = 1, \dots, N$ ) are drawn from a Gaussian process (GP; Rasmussen and Williams, 2005). Then based on the GP posterior, we can design and infer an acquisition function. This acquisition function can be used to determine where
to "look next" in parameter-space, i.e. it can be used to draw  $\mathbf{x}_{N + 1}$  for which we then evaluate the objective function  $f(x_{N + 1})$ . This procedure iterates until a set of optimal parameters is found with some level of confidence.
In practice, the efficiency of BO hinges on multiple choices, such as the specific form of the acquisition function, the covariance matrix (or kernel) of the GP and how the parameters of the acquisition function are estimated. Our objective function is the (importance-sampled) validation NLL, which can only be computed after a model convergences (via gradient-based optimisation of the ELBO). We follow the advice of Snoek et al. (2012) and use MCMC for estimating the parameters of the acquisition function. This reduced the amount of objective function evaluations, speeding up the overall search. Other settings were also based on results by Snoek et al. (2012), and we refer the interested reader to that paper for more information about BO in general. A summary of all relevant settings of BO can be found in Table 8. We used the GPyOPT library (authors, 2016) to implement this procedure.
B Additional Empirical Evidence
In Figure 5 we inspect how MDR and FB approach different target rates (namely, 10, 20, and 30). Note how MDR does so more quickly, especially at higher rates. Figure 6a shows that in terms of validation perplexity, MDR and FB perform very similarly across target rates. However, Figure 6b shows that at the end of training the difference between the target rate and the validation rate is smaller for MDR.
Figure 7 compares variants of SENVAE trained with MDR for various rates: a Gaussian-posterior and Gaussian-prior (blue-solid) to a Gaussian-posterior and Vamp-prior (orange-dashed). They perform essentially the same in terms of perplexity (Figure 7a), but the variant with the stronger prior relies more on posterior samples for reconstruction (Figure 7b).
Finally, we list additional samples: Figure 8 lists samples from RNNLM, vanilla SENVAE and effectively trained variants (via MDR with target rate  $r = 10$ ); Figure 9 lists homotopies from SENVAE models.
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(a) Rate over time for  $r = 10$ .
(b) Rate over time for  $r = 20$ .
Figure 5: Rate progression on the training set in the first 20 epochs of training for SENVAE trained with free bits (FB) or minimum desired rate (MDR). One can observe that at higher rates, FB struggles to achieve the target rate, whereas MDR achieves the target rate after a few epochs.
(c) Rate over time for  $r = 30$ .
(a) PPL  $(\downarrow)$  for various target rates.
Figure 6: Validation results for SENVAE trained with free bits (FB) or minimum desired rate (MDR).
(b) Target rate minus validation rate at the end of training for various targets (lower means better).
(a) Perplexity on validation set: models perform similarly well and perplexity degrades considerably for  $r > 20$ .
Figure 7: Comparison of SENVAEs trained with standard prior and Gaussian posterior (Gauss) and Vamp prior and Gaussian posterior (Vamp) to attain pre-specified rates.
(b) Accuracy gap: VAEs with stronger latent components rely more on posterior samples for reconstruction.
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Model	Sample	Closest training instance	TER
RNNLM	The Dow Jones Industrial Average jumped 26.23 points to 2662.91 on 2643.65.	The Dow Jones Industrial Average fell 26.23 points to 2662.91.	0.23
The companies said they are investigating their own minds with several carriers, including the National Institutes of Health and Human Services Department of Health.,	The Health and Human Services Department currently forbids the National Institutes of Health from funding abortion research as part of its $8 million contraceptive program.	0.69
And you'll have no longer sure whether you would do anything not—if you want to get you don't know what you're,	Reaching for that extra bit of yield can be a big mistake – especially if you don't understand what you're investing.	0.81
SENVAE	The company said it expects to report net income of $UNK-NUM million, or $1.04 a share, from $UNK-NUM million, or,	Nine-month net climbed 19% to $UNK-NUM million, or $2.21 a primary share, from $UNK-NUM million, or $1.94 a share.	0.50
The company said it expects to report net income of $UNK-NUM million, or $1.04 a share, from $UNK-NUM million, or,	Nine-month net climbed 19% to $UNK-NUM million, or $2.21 a primary share, from $UNK-NUM million, or $1.94 a share.	0.50
The company said it expects to report net income of $UNK-Num million, or $1.04 a share, from $UNK-NUM million, or,	Nine-month net climbed 19% to $UNK-NUM million, or $2.21 a primary share, from $UNK-NUM million, or $1.94 a share.	0.50
+MDR training	They have been growing wary of institutional investors.	People have been very respectful of each other.	0.46
The Palo Alto retailer adds that it expects to post a third-quarter loss of about $1.8 million, or 68 cents a share, compared	Not counting the extraordinary charge, the company said it would have had a net loss of $3.1 million, or seven cents a share.	0.62
But Mr. Chan didn't expect to be the first time in a series of cases of rape and incest, including a claim of two,	For the year, electronics emerged as Rockwell's largest sector in terms of sales and earnings.	0.80
+Vamp prior	But despite the fact that they're losing.	As for the women, they're UNK-LC.	0.45
Other companies are also trying to protect their holdings from smaller companies.	And ship lines carrying containers are also trying to raise their rates.	0.60
Dr. Novello said he has been able to unveil a new proposal for Warner Communications Inc., which has been trying to participate in the U.S.	President Bush says he will name Donald E. UNK-INITC to the new Treasury post of inspector general, which has responsibilities for the IRS...	0.78
+MoG Prior	At American Stock Exchange composite trading Friday, Bear Stearns closed at $25.25 an ounce, down 75 cents.	In American Stock Exchange composite trading yesterday, Westamerica closed at $22.25 a share, down 75 cents.	0.32
Mr. Patel, yes, says the music was “extremely effective.”	Mr. Giuliani's campaign chairman, Peter Powers, says the Dinkins ad is “deceptive.”	0.57
The pilots will be able to sell the entire insurance contract on Nov. 13.	The proposed acquisition will be subject to approval by the Interstate Commerce Commission, Soo Line said.	0.60
Figure 8: Sentences sampled from various models considered in this paper. For the RNNLM, we ancestral-sample directly from the softmax layer. For SENVAE, we sample from the prior and decode greedily. The vanilla SENVAE consistently produces the same sample in this setting, that is because it makes no use of the latent space and all source of variability is encoded in the dynamics of its strong generator. Other SENVAE models were trained with MDR targeting a rate of 10. Next to each sample we show in italics the closest training instance in terms of an edit distance (i.e. TER). The higher this distance (it varies from 0 to 1), the more novel the sentence is. This gives us an idea of whether the model is generating novel outputs or copying from the training data.
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Revenue rose  $12\%$  to  $\$ 1UNK$ -NUM billion from  $\$ 1UNK$ -NUM billion.
It is no way to get a lot of ways to get away from its books.
At one point after Congress sent Congress to ask the Senate Democrats to extend the bill.
But the number of people who want to predict that they can be used to keep their own portfolios,
The U.S. government has been announced in 1986, but it was introduced in December 1986
The company said it plans to sell its C$400 million million shares outstanding