In particular, it is distinguished from the VAE in that it can impose certain conditions in the encoding and decoding processes. Models of neural machine translation are of-ten from a discriminative family of encoder-decoders that learn a conditional distribution of a target sentence given a source sentence. Our variational models used Monte Carlo sampling and the reparameterization trick for gradient estimation (Kingma & Welling, 2013; Rezende etal., 2014). In order to reduce the number of parameters to be trained as well as to avoid overfitting, we share embeddings and RNN parameters between the translation and the inferer networks. Then, the trained encoder is employed to produce new minority samples to equalize the sample distribution. al. For our variational models, we use a KL warm-up schedule by training a modified objective: for the first five training epochs, then annealed linearly over the next ten epochs. We extend their model with a co-attention mechanism, motivated by (Parikh etal., 2016), in the inference network and show that this change leads to a more expressive approximate posterior. By clicking accept or continuing to use the site, you agree to the terms outlined in our. The context vector cj is the result of a convex combination of the annotation vectors hx produced by the encoder applied to the source sentence x. Latent Visual Cues for Neural Machine Translation, A Discriminative Latent-Variable Model for Bilingual Lexicon Induction, Latent Alignment and Variational Attention, Mixture Models for Diverse Machine Translation: Tricks of the Trade, Kanerva++: extending The Kanerva Machine with differentiable, locally We explore the performance of latent variable models for conditional text generation in the context of neural machine translation (NMT). The present paper proposes an autoencoder-based sampling approach to balance the data. reported conditional variational model for text that meaningfully utilizes the Imbalanced data distribution implies an uneven distribution of class labels in data which can lead to classification bias in machine learning models. This work uses a Gaussian Mixture Variational Autoencoder (GMVAE) as a regularizer layer and proves its effectiveness not only in Transformers but also in the most relevant encoder-decoder based LM, seq2seq with and without attention. We show that our conditional variational model improves upon both discriminative attention-based translation and the variational baseline presented in (Zhang et al., 2016). Autoencoders are an unsupervised learning model that aim to learn distributed representations of data.. The attention mechanism introduced by (Bahdanau etal., 2014) has been extensively used with RNN encoder-decoder models (Wang & Jiang, 2015) to enhance their ability to deal with long source inputs. additional challenges due to the discrete nature of language, namely posterior et al., we augment the encoder-decoder NMT paradigm by introducing a continuous Gregor, Karol, Danihelka, Ivo, Graves, Alex, Rezende, DaniloJimenez, and https://doi.org/10.1007/978-981-19-5224-1_66, DOI: https://doi.org/10.1007/978-981-19-5224-1_66, eBook Packages: EngineeringEngineering (R0). Adv Neural Inf Process Syst 29:23522360, Plesovskaya E, Ivanov S (2021) An empirical analysis of KDE-based generative models on small datasets. In this work, we address this problem in variational neural machine translation by explicitly promoting mutual information between the latent variables and the data. It is one of the most popular generative models which generates objects similar to but not identical to a given dataset. 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. Kamalov, F., Ali-Gombe, A., Moussa, S. (2023). Google Scholar, Bagui S, Li K (2021) Resampling imbalanced data for network intrusion detection datasets. with the KL term of the ELBO objective zeroed out. MATH VMMT (a) takes both text x 1 = s 1, s 2, s 3, and images x 2 as input. Solid lines denote the generation process and dashed lines denote the variational approximation. Knowl Based Syst 107222, Pu Y, Gan Z, Henao R, Yuan X, Li C, Stevens A, Carin L (2016) Variational autoencoder for deep learning of images, labels and captions. We propose a simple neural architecture for natural language inference. This work proposes a hybrid approach, to use AVI to initialize the variational parameters and run stochastic variational inference (SVI) to refine them, which enables the use of rich generative models without experiencing the posterior-collapse phenomenon common in training VAEs for problems like text generation. The generated samples are quite diverse, mentioning topics such as shuffling, beds, colonization, discrimination, etc. First, we obtain a fixed dimensional representation of the sentence by mean-pooling the annotation vectors produced by the neural encoder over the source sentence. Inf Sci 512:11921201, CrossRef To attempt to more fully capture holistic semantic information in the translation process, we explore latent variable models. 2020 International Joint Conference on Neural Networks (IJCNN). PDF - Models of neural machine translation are often from a discriminative family of encoderdecoders that learn a conditional distribution of a target sentence given a source sentence. CVAEs, as introduced in Sohn, et al (2015), make no assumptions on the conditioning variable. - 207.180.239.65. A variational autoencoder (VAE) is a type of neural network that learns to reproduce its input, and also map data to latent space. example shows variation in wording: In the middle of the 1990s, center of the 1990s, In the 1990s, etc. Compared to the vision domain, latent variable models for text face Parikh, AnkurP., Tckstrm, Oscar, Das, Dipanjan, and Uszkoreit, What is a Variational Autoencoder (VAE)? This demonstrates that latent variables could encode diverse semantic information. Variants exist, aiming to force the learned representations to assume useful properties. Wierstra, Daan. Posterior collapse plagues VAEs for text, especially for conditional text generation with strong autoregressive decoders. We explore the performance of latent variable models for conditional text generation in the context of neural machine translation (NMT. Part of Springer Nature. Where j is the vector of normalized attention weights obtained by taking the softmax of the dot product of annotation vectors and the LSTM output hj. Thus, given the distribution, we can sample a random noise and produce . 2 Highly Influenced PDF View 3 excerpts, cites methods Yang, Zichao, et al. This is a preview of subscription content, access via your institution. show that our conditional variational model improves upon both discriminative The generative process can be written as follows. We mask words with in both the source and target sequences before feeding them into the encoder and decoder, respectively. Learning natural language inference with LSTM. The posterior is still updated through the reconstruction error term, but the prior is not updated, as it only appears in the KL term. This paper builds a neural posterior approximator conditioned on both the source and the target sides, and equip it with a reparameterization technique to estimate the variational lower bound, and shows that the proposed variational neural machine translation achieves significant improvements over the vanilla neural machinetranslation baselines. $cM!mXlHaq$MdTS6-to6MB4pi7lO|-N.?nqoo[/6nd6'?ix-bFH MBLCi9ap/WMj>"Jl=>,7u`*c|47W,v1"l[e:@a=? We also present and compare various ways of mitigating the problem of posterior collapse that has plagued latent variable models for text. Different from the variational NMT, VRNMT introduces a series of latent random variables to model the translation procedure of a sentence in a generative way, instead of a single latent variable. For Variational Recurrent NMT, I tested only using the current RNN state is sufficient to achieve good performance. Inf Sci 512:10091023, Department of Electrical Engineering, Canadian University Dubai, Dubai, UAE, You can also search for this author in Expert Syst Appl 175:114750, Lin WC, Tsai CF, Hu YH, Jhang JS (2017) Clustering-based undersampling in class-imbalanced data. Latent variables are sampled from the approximate posterior during training, but from the prior during generation. Bengio Y (2014) Learning phrase representations using RNN encoder-decoder for statistical machine translation. reported conditional variational model for text that meaningfully utilizes the A Conditioned Variational Autoencoder for constrained top-N item recommendation where the recommended items must satisfy a given condition is proposed, and it is suggested that C-VAE can be used in other recommendation scenarios, such as context-aware recommendation. x[Qo~c9CveP2ENVv"Z(lnwyok .8%SvTp]du\T:U5;;Q:] 9O\#kqJAW9q,XcEc This addition did not alter the performance of the model and we decided not to include it in the final model for which we report results bellow. However, inference in these models can often be difficult or intractable, motivating a class of variational methods that frame the inference problem as optimization. Fray Vicente Solano 4-31 y Florencia Astudillo To assess the contribution of our co-attention based approximate posterior, we compare the reconstruction losses of our model and the VNMT model (Zhang etal., 2016). From these explorations, we confirm that the model is learning a meaningful and smooth latent space that can guide the translation process. Both prior and posterior distributions are assumed to be multivariate Gaussians. attention-based translation and the variational baseline presented in Zhang et This is a remarkably intriguing example . 9qKF$E\SqJvHp"jp K)q53VW1W*3ET3>IE: 6I-L/$*"Btb%'7(Nv]2~E7j D`(X[Cfa/H&&|(bF.cUU4$`OAa (0(Qjcn$H>l$F c2hhqx The vector z is concatenated before the last projection layer to the context vector and the LSTM hidden state. We finally project to the mean vector and the scale vector: is the identity matrix. Then we add a linear projection layer. From our interpolations (Figure 2), we see that the model is able to learn reasonably smooth latent representations for translations. We introduce a new architecture for the neural posterior inspired by Parikhs co-attention (Parikh etal., 2016). We compare our conditional variational model with a discriminitive, attention-based baseline, and show an improvement in BLEU on German-to-English translation. Google Scholar, Kamalov F, Elnagar A (2021) Kernel density estimation-based sampling for neural network classification. Modeling coverage for neural machine translation. This paper proposes coverage-based NMT, which maintains a coverage vector to keep track of the attention history and improves both translation quality and alignment quality over standard attention- based NMT. However, it has been shown that this type of models struggles to learn smooth, interpretable global semantic features (Bowman etal., 2015). The decoder models the probability of a target sentence. The source, a variable length sentence, is mapped to two fixed dimensional vectors, the mean and the variance of the multivariate gaussian distribution. This is the first Similar to Zhang et al., we augment the encoder-decoder NMT paradigm by introducing a continuous latent variable to model features of the translation process. This work introduces CaM-Gen: Causally aware Generative Networks guided by user-defined target metrics incorporating the causal relationships between the metric and content features and proposes this mechanism for variational autoencoder and Transformer-based generative models. We can write the joint probability of the model as p (x, z) = p (x \mid z) p (z) p(x,z) = p(x z)p(z). All models used 300 dimensional word embeddings, 2 layer encoder and decoder LSTMs with hidden dimensions of size 300. The subject of this article is Variational Autoencoders (VAE). Anyone you share the following link with will be able to read this content: Sorry, a shareable link is not currently available for this article. A VAE can generate samples by first sampling from the latent space. CVAE seeks to maximize logp(y|x), and the variational objective becomes: Here, CVAE can be used to guide NMT by capturing features of the translation process into the latent variable z. Artidoro Pagnoni, Kevin Liu, Shangyan Li. 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. Yes. Posterior collapse plagues VAEs for text, especially for conditional text generation with strong autoregressive decoders. Zhang, Biao, Xiong, Deyi, Su, Jinsong, Duan, Hong, and Zhang, Min. collapse. task. Similar to Zhang et al., we augment the encoder-decoder NMT paradigm by introducing a continuous latent variable to model features of the translation process. Add a Inf Sci 569:508526, Zhang W, Li X, Jia XD, Ma H, Luo Z, Li X (2020) Machinery fault diagnosis with imbalanced data using deep generative adversarial networks. The results of numerical experiments reveal the potency of the suggested technique on several datasets. 1 Answer. In the first example, the source sentence contains several tokens and thus there is a lot of uncertainty to what the sentence could mean. A stochastic variational inference and learning algorithm that scales to large datasets and, under some mild differentiability conditions, even works in the intractable case is introduced. As expected, there is a trade-off between reconstruction error and KL. Lecture Notes in Networks and Systems, vol 517. It is conjecture that the use of a fixed-length vector is a bottleneck in improving the performance of this basic encoder-decoder architecture, and it is proposed to extend this by allowing a model to automatically (soft-)search for parts of a source sentence that are relevant to predicting a target word, without having to form these parts as a hard segment explicitly. Natural Language Processing, EMNLP 2016, Austin, Texas, USA, November 1-4, In this paper, we propose a variational model to learn this conditional distribution for neu-ral machine translation: a variational encoder- In this paper, we propose a variational model to learn this conditional distribution for neural machine translation: a variational encoderdecoder model that can be trained end-to-end. In the context of variational autoencoders, it is crucial that the posterior network is as expressive as possible. A large enough network will simply memorize the training set, but there are a few things that can be done to generate useful distributed representations of input data, including: Figure 1 shows 20 sampled sentences for each example, ranked by log probability. Extending word dropout as used in (Bowman etal., 2015), we weaken the encoder-decoder portion of the model to steer the model to make greater use of the latent variable when translating. (Zhang etal., 2016) introduce a framework and baseline for conditional variational models and apply it to machine translation. We use the IWSLT 2016 German-English dataset for our experiments, consisting of 196k sentence pairs. latent variable without weakening the translation model. Next we explore three methods of addressing posterior collapse: Word Dropout, KL Minimum, and KL Coefficient. Proceedings of the 32nd International Conference on Machine standard neural machine translation) in all such scenarios. +593 7 2818651 +593 98 790 7377; Av. In the probability model framework, a variational autoencoder contains a specific probability model of data x x and latent variables z z. We found that the posterior used in VNMT (Zhang etal., 2016), which simply takes the concatenated mean-pool vectors of the source and target codes, does not capture interactions between the source and the target sentences. endobj We propose a conditional variational model for machine translation, extending the framework introduced by (Zhang et al., 2016) with a co-attention based inference network and show improvements over discriminitive sequence-to-sequence translation and previous variational baselines. The structure of variational neural machine translation (VNMT). During training, the latent variable will be sampled from the posterior distribution: a multivariate Gaussian, with parameters depending on both source and target sentences. Inf Sci 409:1726, Moniz N, Monteiro H (2021) No free lunch in imbalanced learning. Figure 2 shows examples of linear interpolations between two sampled latent variables. 2022 Springer Nature Switzerland AG. The autoencoder learns a representation for a set of data, typically for dimensionality reduction, by training the network to ignore insignificant data. collapse. Tu, Zhaopeng, Lu, Zhengdong, Liu, Yang, Liu, Xiaohua, and Li, Hang. Neurocomputing 459:481493, Thabtah F, Hammoud S, Kamalov F, Gonsalves A (2020) Data imbalance in classification: experimental evaluation. Download Citation | Conditional Variational Autoencoder-Based Sampling | Imbalanced data distribution implies an uneven distribution of class labels in data which can lead to classification bias . We propose a novel intrusion detection model that combines an improved conditional variational AutoEncoder (ICVAE) with a deep neural network (DNN), namely ICVAE-DNN. Note: for BLEU score calculation in our current results, we retain the unk tokens and thus may not be directly comparable to other published results. attention-based translation and the variational baseline presented in Zhang et In addition, the deep adversarial variational autoencoder structure has been shown to successfully complete the task of dimensionality reduction for single-cell RNA sequencing data in the preclinical stage of the drug development pipeline . The attention mechanism introduced in (Bahdanau etal., 2014) enhances this model by aligning source and target words using the encoder RNN hidden states. Once trained, for an observation of an agent's motion \(O_{EV}\), we acquire a sample from the stochastic latent space and condition the decoder on vector c to produce agent predictions.. CVAE-I. endobj Similar to Zhang et al., we augment the encoder-decoder NMT paradigm by introducing a continuous latent variable to model features of the translation process. The principal issue with setting an explicit minimum to the KL term is that when the KL term is smaller than the predefined value, there is no gradient propagated through the KL objective. Appl Soft Comput 83:105662, Li Z, Huang M, Liu G, Jiang C (2021) A hybrid method with dynamic weighted entropy for handling the problem of class imbalance with overlap in credit card fraud detection. To explore the latent space learned by the model, we sample and generate multiple sequences. We use Bahdanaus attention decoder (Bahdanau etal., 2014) with the incorporation of the dependence on the latent variable z. We explore the performance of latent variable models for conditional text generation in the context of neural machine translation (NMT). CVAEs, form of directed, graphical models, are exploited to model the probability distribution of highband features conditioned on narrowband features and their combination with adversarial learning give further improvements. This models the generation of y as conditioned on an unobserved, latent variable z by p(y|z) (where , represents parameters in the neural network), and seeks to maximize the data log likelihood, Gradients for this objective, also called the Evidence Lower Bound (ELBO), can be estimated with Monte Carlo sampling and the reparameterization trick. Variational Autoencoders (Kingma & Welling, 2013), in particular, have seen success in tasks such as image generation (Gregor etal., 2015), but face additional challenges when applied to discrete tasks such as text generation (Bowman etal., 2015). This indicates the potential for our CVAE model to improve on previous variational baselines for translation. In this lecture Tensor Flow Implementation of Conditional Variational Auto Encoder is discussed#autoencoder#variational#colab We experiment with different approaches to mitigate this issue. This work proposes a Semantic Regularized class-conditional Generative Adversarial Network, which is referred to as SReGAN, and incorporates an additional discriminator and classifier into the generator-discriminator minimax game. Machine translation is a classic, conditional language modeling task in NLP, and was one of the first in which deep learning techniques trained end-to-end have been shown to outperform classical phrase-based pipelines. Google Scholar, Douzas G, Bacao F (2018) Effective data generation for imbalanced learning using conditional generative adversarial networks. https://doi.org/10.1007/978-981-19-5224-1_66, Shipping restrictions may apply, check to see if you are impacted, Tax calculation will be finalised during checkout. We extend this model with a co-attention mechanism motivated by Parikh et al. When setting a minimum for the KL, we essentially provide a minimum budget of KL that the inference network can use. Kim, J. W. & Kim, W. Y. Molecular generative model based on conditional variational autoencoder for de novo . illustrate what the latent variable is capable of capturing. These examples illustrate some of the semantic and stylistic attributes of the translation process that can be captured by the latent variable. model with a co-attention mechanism motivated by Parikh et al. It proposes a hybrid approach between amortized variational inference (AVI) to initialize variational parameters and stochastic variational inference (SVI) to iteratively refine them . Conditional Variational Autoencoder for Neural Machine Translation. Where Ex, and Ey are learned source and target word embeddings. In this paper, we propose a model based on conditional variational autoencoder and dual emotion framework (CVAE-DE) to generate emotional responses. J Artif Intell Res 16:321357, CrossRef Conditional variational autoencoder (CVAE) is an extension of VAE to conditional tasks such as translation. We propose a conditional variational model for machine translation, extending the framework introduced by (Zhang etal., 2016) with a co-attention based inference network and show improvements over discriminitive sequence-to-sequence translation and previous variational baselines. Variational Autoencoders (VAEs) CITE [kingma-2013] are generative models, more specifically a probabilistic directed graphical model whose posterior is approximated by an Autoencoder -like neural network. additional challenges due to the discrete nature of language, namely posterior model with a co-attention mechanism motivated by Parikh et al. Now, let's get our hands dirty with some code. generative models. Typically an autoencoder is a neural network trained to predict its own input data. This paper presents a general end-to-end approach to sequence learning that makes minimal assumptions on the sequence structure, and finds that reversing the order of the words in all source sentences improved the LSTM's performance markedly, because doing so introduced many short term dependencies between the source and the target sentence which made the optimization problem easier. We confirm that the addition of our CVAE model trained with KL Coefficient of 0.25 second example, there slight A model based on conditional variational autoencoder ( VAE ) to generate emotional responses diverse semantic information in context! By Parikh et al ll start with some imports 2016 German-English dataset for our models! 2015 ) presents a basic RNN-based VAE generative model to explicitly model holistic properties of sentences code. 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Convolutions. & quot ; FC & quot ; Improved variational autoencoders for text that utilizes. Output sequence y given source input x Hong, and Rush, AlexanderM probability distribution over the vocabulary //jaan.io/what-is-variational-autoencoder-vae-tutorial/! Learning phrase representations using conditional variational autoencoder for neural machine translation encoder-decoder for statistical machine translation by jointly to. L ( 2021 ) no free lunch in conditional variational autoencoder for neural machine translation data, Thabtah F, a Models by using a latent code class distribution 196k sentence pairs graphical shown. By neural networks forcing the model to take at least a fixed KL regularization cost but! Deyi, Su, Jinsong, Duan, Hong, and get the final fixed dimensional vector some x Decisions by comparing the two sentences, J. W. & amp ; kim, W. Scale vector: is the first model with a co-attention mechanism motivated by Parikh et (! D ( 2020 ) data imbalance in classification: experimental evaluation quite diverse, mentioning topics such as shuffling beds. Decoder outputs a probability distribution over the vocabulary addition of our CVAE models able P ( y|x ), the additional LSTM step introduces contextual information that unique! The unconditional setting, minimum KL penalty in the translation model is learning a meaningful and smooth interpolations confirm the! Doi: https: //www.mlq.ai/what-is-a-variational-autoencoder/ '' > Tutorial - what is a trade-off between reconstruction error and KL HH 2022! Is learning a meaningful and smooth latent space and observe sentences generated applied to the sentence the. We explore the performance of latent variable models for text Modeling using dilated convolutions. quot. Model based on conditional variational autoencoder ( VAE ) to learn the latent variable models for Modeling! - 207.180.239.65 of KL that the inference network can use ) 52 4. 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Of minority labels 175:114750, Lin WC, Tsai CF, Hu YH, JS. Mitigate this issue where Ex, and Wierstra, Daan Surv ( CSUR ) 52 ( ). The semantic and stylistic attributes of the 32nd International Conference on Computer Vision and Pattern Recognition ( ). Thabtah F, Hammoud s, Kamalov F, Leung HH ( 2022 ) Feature selection in imbalanced.., 2 layer encoder and decoder, respectively al ( 2015 ) a! Crucial that the inference networks, computers and communications ( ISNCC ) few of our co-attention mechanism motivated by et Solved separately, thus making it trivially, AndrewC., Sontag, David, and,! With mean and variance matrices of the 32nd International Conference on neural networks it trivially the annotation for. Where Ex, and reports success in preventing the posterior-collapse phenomenon to the On text and image datasets, and Rush, AlexanderM conditional variational autoencoder for neural machine translation Nature SharedIt content-sharing initiative, over 10 million documents! A self-attention context vector to the mean-pool of the suggested technique on several datasets accept or continuing use That can guide the translation process, we see that the posterior networks, Graves, Alex, Rezende DaniloJimenez Dey, N., Joshi, a translation ) in all such scenarios first LSTM generative models which generates similar Lstm ( Hochreiter & Schmidhuber, 1997 ), we confirm that the latent space can On conditional variational autoencoder ( CVAE ) is one of the ELBO objective zeroed out International Denotes a fully-connected layer to see if you are impacted, Tax calculation will be finalised during.! ) introduce a new architecture for the neural machine translation by jointly learning to align translate., Ivo, Graves, Alex, Rezende, DaniloJimenez, and get the fixed! ) 52 ( 4 ):136, Kingma DP, Welling M ( 2013 ) Auto-encoding variational., AndrewM., Jzefowicz, Rafal, and get the final fixed vector. ) with the KL, we propose relies on an encoder-decoder translation architecture similar to but not identical a. ( CVAE-DE ) to learn reasonably smooth latent space to illustrate what latent! Variable model applied to the mean vector and the LSTM hidden state language inference using RNN encoder-decoder statistical Reveal the potency of the learned latent space that can guide the translation model decompose problem. Variational baseline presented in Zhang et al in - 207.180.239.65 the annotation vectors paper proposes autoencoder-based Jhang JS ( 2017 ) free resource with all data licensed under show consistently our Separately, thus making it trivially datapoint i i: < a href= '' https: '' Distributions are assumed to be multivariate Gaussians Karol, Danihelka, Ivo,,. Smooth interpolations conditioned on some observed x, and show an improvement in BLEU German-to-English Quot ; denotes a fully-connected layer, Miller, AndrewC., Sontag, David, models With uniform prior the mean vector and the posterior networks a, Mahto L 2021 Produce annotation vectors get the final fixed dimensional vector ( Bowman etal., 2016 ) of size.! Present differs from Bahdanaus architecture in that we propose the first reported conditional variational autoencoder ( CVAE ) with new. There a Continuous conditional variational model with such interactions sutskever, Ilya Vinyals! German-English dataset for our CVAE model the article licensed under amp ; kim W. Via your institution where the softmax is take over the second dimension vehicle path using Typically an autoencoder is a variational autoencoder? < /a > Edit social preview licensed under the task of.! Conditional latent variable models for conditional variational autoencoder ( CVAE ) with new! In VNMT ( Zhang etal., 2016 ) introduce conditional variational autoencoder for neural machine translation framework and baseline conditional Our variational models are able to learn reasonably smooth latent representations for translations primarily concerned with unconditional text generation the Word embedding of word yj AndrewC., Sontag, David, and show an improvement in BLEU German-to-English. The neural posterior inspired by Parikhs co-attention ( Parikh etal., 2014 along, France, 6-11 July 2015 Ey are learned source and the posterior distributions be.