SUNMASK: Mask Enhanced Control in Step Unrolled Denoising Autoencoders
Anonymous Author(s)
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Abstract
This paper introduces SUNMASK, an approach for generative sequence modelingbased on masked unrolled denoising autoencoders.
By explicitly incorporating aconditional masking variable, as well as using this mask information to modulatelosses during training based on expected exemplar difficulty, SUNMASK modelsdiscrete sequences without direct ordering assumptions.
The addition of maskingterms allows for fine-grained control during generation, starting from randomtokens and a mask over subset variables, then predicting tokens which are againcombined with a subset mask for subsequent repetitions.
This iterative processgradually improves token sequences toward a structured output, while guided byproposal masks.
The broad framework for unrolled denoising autoencoders islargely independent of model type, and we utilize both transformer and convolutionbased architectures in this work.
We demonstrate the efficacy of this approach bothqualitatively and quantitatively, applying SUNMASK to generative modeling ofsymbolic polyphonic music, and language modeling for English text.
Introduction
Generative modeling approaches can stratified into different modeling approaches based on factorization to form two broad categories, autoregressive modeling (AR) and non-autoregressive modeling(NAR).
We introduce SUNMASK, a NAR generative model for structured sequences.
1.1 Autoregressive Models
AR modeling with deep neural networks has been a dominant approach to generative modelingand feature learning [38, 70, 73, 39, 76, 74] which has many crucial advantages in both trainingand inference.
One key concern is the necessity of defining a "dependency chain" in the form ofa (typically) directed acyclic graph (DAG).
Sampling during inference can be accomplished in astraightforward manner using ancestral sampling - sampling from the first variable or variables in the
DAG, using those to conditionally estimate a probability distribution for subsequent variables.
Many applications have straightforward orderings in which to define this chain of variables, basedon domain knowledge.
For example following the flow of time for timeseries modeling is oftena logical choice, allowing models to make predictions into the future from the past. However inmany other domains, for example images, language, or music, the process of defining a dependencychain over input variables (e.g. pixels, characters, words, or notes) is far from straightforward, as for any arbitrary ordering there can frequently be examples where this ordering creates long-termdependencies, or otherwise makes satisfaction of dependencies during training and evaluation moredifficult than another alternative ordering.
This divide becomes further compounded in many creative applications to these domains, as creators typically iterate repeatedly: forming a concept, applying an initial sequence of steps to create the framing of the concept, and seeing where the creative flow may lead to alterations in the originalconcept, thus altering future steps.
Though the resulting output may be perceived in a time-orderedfashion (for example, reading a book or listening to a song), the initial creation was performedglobally and holistically.
This global view is often critical to creating elements such as foreshadowingand tension which make the resulting output interesting or enjoyable.
This iterative process is directlyat odds with a strict AR factorization, and requires well trained AR models to cope with a high degreeof uncertainty and multi-modality for long range dependencies, which can lead to logical mistakes orother errors.
1.2 Non-Autoregressive Models
An alternative methodology for generative modeling is non-autoregression (NAR), broadly covering alarge number of different modeling approaches which attempt to remove assumptions about variableordering, instead either hand-defining per-exemplar orderings, or modeling variables jointly withoutresorting to chain rule factorization.
One way to define an ordering over variables is via maskingof inputs or intermediate network representations [22, 71, 72, 77, 73, 57], and indeed modern ARapproaches such as transformers [75] use an autoregressive mask internally to define the chain ofvariables order.
These masks can either be constant over all training (as in standard AR transformersand PixelCNN [73]) or dynamic per example (as in MADE [22]).
When masks are dynamic perexample, we begin to see the relationship between enforcing AR via masking and NAR methods, asalthough some ordering is assumed this ordering is no longer constant, and it becomes possible touse the same trained model to evaluate the probability of a particular output variable under multiplepossible orderings.
Closely linked to masking methods are so called diffusion models , which relax the variable orderingproblem through noise prediction [67, 69, 30].
Rather than predicting a new variable or variablesgiven previous ones in an arbitrarily chosen DAG, diffusion models focus on predicting a lessnoisy version of many variables jointly, given a set of noisy input variables.
Iteratively applyingthis learned denoising improvement operator should eventually result in predicting a fully cleanoutput estimate, given either a noisy version of the target domain, or even starting from pure noise.
Given this framing it is clear that diffusion models are closely linked to denoising methods ingeneral, specifically denoising autoencoders, as well as modern density modeling approaches suchas generative adversarial networks (GAN [23]), variational autoencoders (VAE [41]), flow-basedmodels (NICE [15], RealNVP [16], Normalizing Flows [61], IAF [42], MAF [57]), iterative canvassampling (DRAW [24]), and noise contrastive estimation (NCE [27]).
Particular applications ofthis denoising philosophy such as BERT [14], WaveGrad [9], and GLIDE [56], have resulted inlarge quality improvements for feature learning and data generation for text, images, and audio[46, 66, 60, 29].
1.3 Trade-offs Between AR and NAR Approaches
The choice between AR and NAR methods is not clear-cut.
For many domains, high-quality modelsexist using both approaches but we can define some crucial parameters.
Some NAR methods such as
GAN or VAE are capable of generating output in only one inference step, however they are typicallyhard to train on certain data modalities (e.g. text data) comparing to AR counterparts.
Other NARmethods such as diffusion models typically allow for choosing a diffusion length during inference,which is independent of that used at training.
Choosing a low diffusion length can frequently lead topoor sample quality, and tuning this setting (among many others) is critical to high quality generation.
However if the tuned diffusion length for a given sequence (of length T ) is shorter than the lengthof those sequences, the NAR method has a computational advantage over the equivalent AR model(which would require T steps for a T length sequence).
In addition, the ability to tune this diffusionlength can be useful in interactive applications, or when a variety of output is desirable.
Thissetting can also be a curse, as even well-trained models perform poorly with improper diffusionsettings.
Several branches of current research are focused on improving guarantees [35, 68, 40] andconvergence speed for diffusion models [43, 44, 36, 79].
1.4 SUNMASK, a non-autoregressive sequence model
We introduce SUNMASK, a NAR sequence model which uses masks over noised, discrete datato learn a self-improvement operator to transition from categorical noise to the data distribution in iterated steps.
Given a target data representation, we train a model which can map from a noisyversion of input data to a corrected form of the input.
In this work, we use multinomial noise - namelyentries are corrupted to 1 of P possible values (for a given set size P ), with the number of noisedentries defining the relative noise level for the training example.
This is similar to many diffusionapproaches at a high level, and particularly shown to be an effective tool in SUNDAE [65] and Coconet [33].
In addition to the use of multinomial noise, we also form a mask representing wherethe data was noised, feeding this mask alongside the input data to form a conditional probabilitydistribution.
Method
The relationship between discrete diffusion and denoising autoencoders has been explored in previouswork [31, 65, 3, 32].
We build upon this foundation, combined with many insights from priororderless modeling approaches, crucially Orderless NADE [72], Coconet [33] (which is a moremodern variant of Orderless NADE), and SUNDAE [65].
SUNMASK is built around a process x t ∼ f θ ( ·| x t − 1 ; m ) on a space X
= { 1 , . .
. , v } N of arrays ofcategorical variables.
This parametric transition function f θ takes an additional argument m ∈ 0 , 1 N .
During training, m indicates variables that were not corrupted, and as a consequence we can use itduring inference to tell f θ which variables to trust.
Given a sequence of masks m 0 , . . .
, m T − 1 , the generating distribution of our model derives from aprior p 0 (typically uniform noise) and repeated application of f θ :
[Equation]
In practice, p 0 is typically elementwise iid uniform noise, and the masks m 0 , . . .
, m T − 1 are drawnaccording to a schedule and may be held constant for several steps.
To train f θ , we take a training example x ∼ p data and draw a mask m .
We apply the corruptionprocedure x 0 ∼ q ( ·| x ; m ) to obtain x 0 which equals x where the mask m is true and uniform randomvalues elsewhere.
Then we iterate x t ∼ f θ ( ·| x t − 1 ; m ) with the aim of reconstructing x .
As in SUNDAE, the transition f θ models the variables as conditionally independent of one another.
However SUNDAE has no direct concept of masking.
SUNMASK thus combines past insights fromthe masked NAR models Orderless NADE and Coconet with existing concepts from SUNDAE, alongwith new model classes and inference schemes to form a powerful generative model.
Similar to SUNDAE, our objective is to minimize 12 ( L (1) + L (2) )
where
[Equation]
is the reconstruction loss for the elements of x that were corrupted.
As in Orderless NADE [72]and Coconet [33], we weigh each term according to the size of the mask, to ensure that the overallweight on each conditional f ( i ) θ is uniform across i .
Unlike previous methods, we target only maskedvariables in the loss.
In practice we choose m 0 = · · · = m t − 1 during training and t = 2 . Since weonly go to t = 2 , keeping the mask constant is a close enough approximation to the masking scheduleused in inference.
The choice of t = 2 is driven by the ablation study in SUNDAE, where t = 2 wasfound to account for nearly all performance gains in translation experiments, with higher unrollingsshowing no additional benefit.
In addition higher values of t unrolling generally increase memoryusage, making the training of high order unrollings complicated.
Coupled with multi-step unrolling, the SUNMASK training scheme encourages learning complexrelationships between the mask and the data, allowing the potential for multi-level trust over the inputdata: variables with a mask value of 1 which appear correct (given context), variables of mask value 1which appear incorrect, variables of mask value 0 which appear correct, and variables of mask value0 which appear incorrect.
Denoising only methods (such as SUNDAE [65]) would need to forman internal, non-controllable mask in order to disentangle these states, and 0 mask models (such as
Coconet [33]) have controllable input masks but combine both masked states.
SUNMASK allows for direct control at inference using both proposal masks and noising of variables,combining elements of both SUNDAE and Coconet.
We show a high level example of the unrolledtraining scheme, mask proposals, and input data processing in Figure 1.
2.1 SUNMASK, SUNDAE, and Coconet Comparison
The overall unrolled mask and iterative inference setting is largely independent of architecture choice,and as long as the internal architecture does not make any ordering assumption over the input datawe can incorporate it into SUNMASK.
We use two primary archetypes for the internal model inthis paper: Attentional U-Net and Relative Transformer.
Detailed description of the respectivearchitectures can be seen in the Appendix.
SUNMASK uses an unrolled training scheme, similar to that shown in SUNDAE, as well as a maskwhich is input to the model and defines manipulated variables as in Coconet.
The loss is masked basedon this manipulation mask, unlike Coconet or SUNDAE.
The SUNMASK loss is further weighted bythe total amount of masked variables.
Comparisons of various high level modeling features between
SUNMASK, Coconet, and SUNDAE are shown in Table 1.
[Table]
2.2 Model Training
During training, the internal architecture is combined with a step unrolled training procedure, ashighlighted by SUNDAE [65].
Rather than directly randomizing positions, we re-write this as amasking scheme, first sampling a mask (with 0 randomize, 1 keep, which we denote as 0 -activeformat) then performing randomization to one of P possibilities, for the masked subset of K variables.
This random masking procedure is equivalent to the approach from SUNDAE, but using a maskallows us to further combine the mask information with the input data, in order to form a conditionalprobability estimate.
In addition, this 0 -active masking scheme makes direct comparison to maskingschemes with absorbing states (such as OrderlessNADE [72], Coconet [33], VQ-Diffusion [25] and OA-ARDM [31]) simpler, as the mask can be directly multiplied with the data in a 0 -active format.
Convolutional SUNMASK incorporates mask information with a one-hot data representation byconcatenation along the channel axis.
Transformer SUNMASK uses a slightly different setting - assuming input transformer data ( T , B ), with T timesteps, B batch elements, and vocabulary size Pis transformed to a ( T, B, L ) dimensional embedding, we repeat the mask along the embedding axis
L times, downweighting the values in the mask by 1 √ L for numerical reasons.
Concatenating thisreduced mask with the input embedding along the last axis is sufficient to form the desired conditionalprobability distribution.
This stretched and reduced mask format provides more stable training thanother schemes such as separately embedding the mask, then concatenating or summing with the inputdata embedding.
Each training batch is randomly sampled from the training dataset, and a corresponding noise valuedrawn from rand ( N ) for N examples in the minibatch.
This per-example noise value is then usedto derive a per-step mask over T timesteps, by comparing noise rand ( N ) < rand ( N, T ) .
Duringtraining, this means some examples have a high per-example noise value (e.g. . 99 ), and thus manyvalues masked and noised in the training, while other examples may have a low noise value (e.g.. 01 ) drawn instead.
Combined with a training loss which learns to denoise the input and focuses onimputing information about masked corrupted inputs, the overall model will learn a chain to go frommore noisy data to less noisy step-wise, resulting in a learned improvement operator [32, 65].
This improvement operator can be applied to noisy data or pure noise, and iterate toward a predictivesample from the training distribution.
See Multinomial Diffusion [32] and SUNDAE [65] for moredetail on this proof, as well as fundamental work on denoising autoencoders [1].
In SUNMASK, wecombine the mask used to noise the input with the input data itself, while modifying the loss to predictonly masked variables .
In addition, we downweight the loss by [Equation] 1 − m t for each batch element,meaning that losses for heavily masked entries are downweighted compared to losses on exampleswith little masking, in a form of curriculum weighting based on expected estimation difficulty.
While a one step denoising scheme can be sufficient for learning the data manifold [3, 1], unrollingthis denoising scheme into a multi-step process can have performance benefits.
SUNMASK directlyuses the unrolled loop scheme described in [65], using a step value of 2 .
For a detailed description ofthe step unrolled training scheme, see Appendix or the overview description from SUNDAE [65].
The masked and unrolled training can be seen as a container for any internal model which doesnot make ordering assumptions, and we utilize both convolutional U-Net (a variant of GLIDE [56]
U-Net) and Relative Transformer [12, 34, 59] models for various experiments, shown in Section 4.
2.3 Inference Specific Settings
Well-trained SUNMASK models should be applicable to full content generation, as well as a varietyof partially conditional generative tasks such as infilling and human-in-the-loop creation.
Basicsampling involves creating a set of variables, with all variables randomly set to 1 of P values in thedomain (or partial randomization in the case of infilling) along with an accompanying mask, which isinitially all 0 for full generation, or mixed 1 s and 0 s for partial generation tasks.
Given this data andmask as input, the trained model then predicts a probability distribution over all possible P values, forall variables.
Despite the use of masked losses in training, we sample these prediction distributionsfor all variables.
These predictions are then accepted or rejected from the original set, resulting in anew variable set.
We then sample a new mask (based on a predefined schedule) and combine it withthe initial mask, then iterate this overall process, updating at least some of the variables at each step.
During inference we use several key techniques to improve generative quality.
We use typicalitysampling [52] on the output probability distribution and a variable number of diffusion steps, onthe order of 100 to 2000 .
Masks are randomly sampled using the schedule defined in [33] whichlinearly decreases the number of masked variables over time according to α n = max( α min , α max −nηN ( α max − α min )) with α min = .
001 , α max = .
999 , and η = 3 / 4 as in [33], along with an optionaltriangular linear ramp-up and ramp-down schedule for the probability of accepting predictions fromthe model into the current variable set at each step, as shown in [65].
Active balance, by increasingthe probability of updating variables which have been updated less often, is another inference timeoption.
Variables can also be re-noised at any step, randomly resetting any variable with a 0 valuefor the mask at that diffusion step.
Some problems (primarily symbolic music modeling) showedincreased variability from active balance and re-noising, but the usefulness of these options is taskdependent.
We caution that tuning hyperparameters for inference is critical to success, as improper settings candrastically lower the performance of SUNMASK, see Section 4 for variance over various inference settings in different tasks.
For human-in-the-loop applications, the existence of these controls canallow a number of fine-grained workflows to emerge, driven by expert users to create and curateinteresting output [18, 11], demonstrated in Figure 3.
Related Work
We state here some key related approaches, as well as how our method differentiates from theseprevious settings.
A number of recent publications on diffusion models and feature learning haveincorporated masks as part of their overall training scheme [31, 7, 29], however these papers usemasks for blanking, rather than as indicators over stochastic variables.
Many infilling models [17,37, 14, 50, 10], and masked image models [29] feature conditional modeling with a mask (blank)token, predicting the variables masked from the input for feature learning or generative modeling.
XLNet [77] combines the infilling and autoregressive paradigms, learning arbitrary permuted ordersover masked out variables, using blank-out masking and randomly generated autoregressive orderingsimilar to OrderlessNADE and Coconet.
Conditional diffusion generators [53, 63, 64] and GANgenerators [20] have the combination of mask indicators as well as preserving stochasticity of themasked variables.
However these methods do not use an unrolled training scheme, and generallytarget image related tasks, with the notable exception of maskGAN.
Many models use a concept of aworking canvas, and do repeated inference steps for generation or correction of data [24, 4, 45, 21,55], SUNMASK differs from these models due to architecture choices, training scheme, and lossweighting, as well as application domain [54, 58, 62, 25, 56, 60].
3.1 Convolutional SUNMASK
SUNMASK is most closely related to coconet [33] and SUNDAE [65].
Coconet (as an instance of
OrderlessNADE)
, trains by sampling a random mask per training example, using this mask to setpart of the input (in one hot format) to zero.
The mask is further concatenated to the zeroed dataalong the channel axis, and this combined batch is passed through a deep convolutional networkwith small 3 × 3 kernels.
Convolutional SUNMASK uses a downweighted loss over only variablesmasked in the input.
However, SUNMASK additionally uses the unrolled training scheme, as well asa different inference procedure due to preserving the values of masked out variables during trainingand sampling.
Our best performing convolutional SUNMASK architecture takes hints from recent image transformerand vector quantized generators, exchanging the small kernels used in Coconet for extremely largekernels of shape 4 × P over the time and feature dimensions, somewhat analogous to input patches,removing the model’s translation invariance over the feature axis by setting kernel dimension equalto the total feature size.
However this makes the number of parameters per convolutional layerextremely large.
Convolutional SUNMASK adopts an attentional U-Net structure which reduces onlyacross the time axis, modified from GLIDE [56], rather than the deep residual convolution networkused by Coconet.
Combined with the addition of step unrolled training, we are only able to trainconvolutional SUNMASK with a batch size of 1 (expanded to effective batch size 2 due to stepunrolling) on commodity GPU hardware with 16 GB VRAM.
Due to the design choice of extremely large kernel sizes which depend on the size of the domain, weonly use convolutional SUNMASK for polyphonic music experiments, see Section 4 for more details.
Exact specification of the convolutional U-Net architecture can be seen in the Appendix.
3.2 Transformer SUNMASK
Transformer SUNMASK relates closely to the transformer used in SUNDAE.
The architecture uses arelative multi-head attention [12, 34] and has no autoregressive masking.
SUNMASK transformeralso uses larger batch sizes, typically 20 or larger, though this is far smaller than the batch sizesseen in the experiments of SUNDAE.
Sequence length and data iterator strategy were both a criticalaspect for training transformer SUNMASK.
We found short sequences (from 32 to 128 ) worked best,along with iteration strategies that were example based.
In the language experiments, padding eachexample to some max length resulted in more stable training than the typical language modelingapproach of treating the corpus as one long sequence and slicing into even sized chunks, or iteratingsequentially.
The stability gap between padded sequences and non-overlapping chunking becameespecially apparent at sequence lengths above 128 with transformer SUNMASK.
[Table]
We list the hyperparameters for the transformer SUNMASK models in the Appendix.
Transformer
SUNMASK was trained on every dataset used in this paper, and we show performance in Section 4,as well as comparisons to convolutional SUNMASK on symbolic polyphonic music modeling.
Bothconvolutional and transformer based SUNMASK use the Adam optimizer, with gradient clipping byvalue at 3 .
Inference hyperparameter types and general sampling strategies used are the same withboth models, though specific hyperparameter values may change between datasets.
Experiments
4.1 Quantitative Results
We demonstrate the use of SUNMASK for polyphonic symbolic music modeling on the JSB dataset [2,5].
The JSB dataset consists of 382 four-part chorales, originally written by Johann Sebastian Bach.
These chorales are quantized at the 16 th note interval, cut into non-overlapping chunks of length 128 ,skipping chunks which would cross the end of a piece.
This processing results in a training datasetof 4956 examples, with each example being size (4 , 128) .
We train convolutional and transformerversions of both SUNMASK and SUNDAE for comparison, as well as the pretrained Coconet [33].
For polyphonic music, the quantized data was rasterized in soprano, alto, tenor, bass (SATB) order, asin Music Transformer [34] and BachBot [47], then chunked into non-overlapping training examples.
Results are shown in Table 2.
These results are evaluated on Bach ground truth data (Bach GT),
BachMock Transformer (BachMock [19, 49]) (closely related to the decoder from VQ-CPC [28]), Coconet, SUNDAE (SD), and SUNMASK convolutional (SMc) and SUNMASK transformer (SMt).
Model variants are indicated with Active Balance (A), Typical Sampling (T), and Noise (N).
The grading function used for evaluation, referred to as BachMock here, is designed specifically tocorrelate with expert analysis on Bach. In particular using this metric to choose correct examples in apaired comparison test, outperforms novice, intermediate, and expert listeners by varying margins [19]. This indicates that scoring well on the aggregate metric should correlate to high sample quality. Themetric has many sub-parts, ranking various musical attributes crucial to codifying the style of J.S.
Bach.
AugGen [49] incorporated this metric into an iterative training and sampling scheme whichimproved final generative capability for a fixed model, showing the effectiveness of BachMock inpractice for ranking machine generated samples.
For every grading function in Bach Mock gradingfunction, we show the median value and ± standard deviation as well as the overall grade, and lowervalues are better. We see the strongest results for convolutional SUNMASK with typicality sampling. Combined with final top-N ( N = 20 ) selection out of a candidate set of 200 samples, the overallsample quality outperforms strong baselines. In addition, the massive performance gain from top-Nselection indicates that the variance is likely driven by failures during sampling, rather than morefundamental modeling errors.
The EMNLP 2017 News dataset is a common benchmark for word-level language modeling [6],containing a large number of news article sentences [51].
Preprocessing steps collapse to sentencescontaining the most common 5700 words, resulting in a training set of 200 k sentences with a test setof 10 k .
The overall maximum sentence length is 51 .
Common processing for this dataset includes padding all sentences up to this maximum length, different than the standard long sequence chunkingcommonly used in other language modeling tasks.
We show the results of several SUNMASK models for generating sentences similar to EMNLP2017News, comparing to benchmarks using the standard Negative BLEU/Self-BLEU evaluation [80, 6] over generated corpora of 1000 sentences in Figure 2.
This set of scores, varied acrosstemperature, is compared against baseline scores [48, 78, 8, 26, 13, 75], similar to the evaluationshown in SUNDAE [65]. These reference benchmarks used 10000 sentences to form performanceestimates.
4.2 Qualitative Study of Masking For General Task Control
Given the flexibility of masking at inference, we perform a number of qualitative queries to inspecthow the model adapts based on noise and mask value.
Figures 3 4, and 5 demonstrate the use of
SUNMASK for musical inpainting, holding the top voice (soprano) either fully or partially fixed tothe well-known melody "Ode to Joy", by Ludwig van Beethoven.
We see the trained model is morethan capable of inpainting based on a pre-defined mask.
We test masks which hold the whole soprano fixed, masks which cover only parts of the soprano butdo not allow randomization away from those notes, and masks which cover parts of the soprano butallow rewriting of the non-masked parts of the soprano, as well as rewriting all other voices.
The useof masks to focus on subsets of variables while preserving underlying intermediate predictions is unique to SUNMASK, as SUNDAE does not have an explicitly controllable input mask and Coconetdoes not have the ability to mask without also blanking the underlying variable.
This control is also demonstrated in Table 3, where masking is used to variably increase or decreasethe weight on various pre-specified terms, held fixed throughout inference.
The combination of thesewords, and their mask status can be seen to influence the overall tone of the selected text passageswhich showed the strongest effect in a particular inference batch.
Though the generation quality isflawed, we clearly see a relationship between the masked word and the emergent surrounding context,for example highlighting war draws forth divorce, attack, and leave, while play instead references
Premier Cup and excitement.
We see similar results on a batch scale, and full demonstration of thetext samples can be seen in the Appendix.  Conclusion
We introduce SUNMASK, a method for masked unrolled denoising modeling of structured data. SUNMASK separates the role of masking and correction by conditioning predictions on the mask,allowing for fine-grained control at inference. When applied to text as well as symbolic polyphonicmusic, SUNMASK is competitive with strong baselines, outperforming reference baselines on musicmodeling. Leveraging the separation of mask and noise allows for subtle control at inference, pavingthe way for a variety of domain specific applications and generative pipelines for human-in-the-loopcreation.
[Table]
I think I want to leave success at the end of the disaster , but because that ’ s a nice to say it ’ s not good to be the challenge and this is a very good thing <eos>
That was the job I was success to have to pay my disaster but hopefully I have been able to pull playing in the first couple of the season , I ’ ve been happy to go through this team , he said <eos>
[Table]
Although more than 80 , 000 success have been displaced in the disaster since the last year , more than 700 , 000 lives have been injured in the country , and 70 of them were killed , according to the UN media <eos>
I haven ’ t had a success at the league , the disaster and picked running with the door ago we have Champions , and I was a couple of pressure . . .
and it was a lot of times <eos>
Celebration unmasked crime masked
This is a fact on the celebration is such a really good crime , but it can be some of the most good people around the world and I think it must be the good way to do it <eos>
It ’ s part of those celebration at the start of the crime , and it ’ s a lot of good pride over the past few years , it ’ s going to be more happy to play through this world <eos>
Celebration masked crime unmasked
Last year , the numbers of celebration applications have been adopted in crime since the
Middle since since year has watched a rate of more than 95 per cent in the UK since 2011 , 2015 to 45 per cent <eos>
The Prime Minister has been a celebration to the course of the crime deal which and to have a relationship of the European Union , with the rest of the European Union has before the scandal <eos>
War unmasked play masked
The Prime Minister David Cameron said war had not be hard to play the divorce of the
European Union , and determined it would mark a divorce between the majority of the UK and leave the bloc of the European Union <eos>
It may be really more special war . .
try to play it , and I hope we ’ re going to be able to attack this team so we have to do it <eos>
War masked play unmasked
But I was proud of the war I ’ ve got to play to it
but I wish that ’ s because I want to do , and
I ’ m pretty excited before the end of the season , he said <eos>
We are in the Premier Cup war and we want to keep play with their top six that which we need to play in the World Champions and at the end of the season that we have to be it well <eos>
Unconstrainedgenerations
By the time , the driver had been deployed to lie out to the incident , an new official said that the woman had not been found a them <eos>
According to The Wall Post survey poll found that 80 per cent of eligible older registered who , they thought he would be the rate for 10 per cent less likely to vote , while 16 per cent of those said they would still less likely to happen <eos>
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